Comparative Analysis of Soft-Body Physics Simulation Fidelity in AI-Generated Beach Locomotion Sequences: A Multi-Platform Biomechanical Assessment

Abstract

Recent advances in AI video generation have raised fundamental questions regarding the fidelity of biomechanical simulation, particularly in scenarios involving complex soft-tissue dynamics during human locomotion. While generative models have demonstrated impressive capabilities in environmental rendering and gross motor animation, the specific challenge of realistic soft tissue deformation—governed by mass-spring-damper physics rather than learnable pixel correlations—remains inadequately characterized in the literature. This study evaluates eleven leading video generation platforms (Google Veo, Sora 2, Seedance, Kling, Runway, Grok, Wan, PixVerse, Blueberry, Peach, and Kiwi) across twenty-one model configurations using standardized methodology. A keyframe extracted from professionally produced control footage was provided to each platform alongside a prompt engineered to specify biomechanically accurate soft tissue dynamics using clinical terminology. Generated outputs were evaluated against control footage across six criteria: hair dynamics, soft tissue physics, gait biomechanics, fabric independence, environmental interaction, and temporal consistency. Results indicate substantial performance stratification, with Kling 2.6 achieving exceptional fidelity including complex secondary tissue deformation, while other platforms ranged from moderate approximation to catastrophic anatomical failure. Two platforms (Sora 2, Runway Gen 3) rejected the prompt on content policy grounds despite clinical framing. Notably, top-performing models demonstrated learned physical priors that override input-specific visual information, generating natural tissue dynamics from a keyframe depicting apparently augmented anatomy. Content moderation strategies varied from hard blocking to undocumented physics attenuation and selective dynamics suppression. These findings establish soft tissue simulation as a meaningful capability dimension for platform evaluation and highlight the complex interplay between technical capability and content policy in shaping generative video output.

Introduction

The rapid advancement of AI video generation systems has produced remarkable capabilities in synthesizing realistic visual content from text and image inputs. Contemporary platforms demonstrate convincing performance across diverse domains: environmental landscapes, architectural spaces, vehicle motion, and increasingly, human figures. However, the fidelity of human figure animation varies substantially depending on which aspects of human motion are under evaluation.

Gross motor patterns—walking, running, gesturing—have received significant attention in model development and benchmark evaluation. A figure that moves through space with anatomically plausible limb coordination represents a baseline capability that leading platforms now achieve with reasonable consistency. Yet human motion encompasses more than skeletal articulation. The human body is composed of heterogeneous tissues with distinct mechanical properties: rigid bone, contractile muscle, elastic connective tissue, and deformable adipose deposits. Realistic human animation requires accurate simulation not only of the skeletal system but of how soft tissues respond to the forces generated by that system.

Soft tissue dynamics present a particular challenge for diffusion-based video generation. These models learn statistical correlations between pixel arrangements across frames, effectively predicting “what typically comes next” given current visual state. This approach succeeds when the target phenomenon is well-represented in training data and does not require explicit physical modeling. For soft tissue deformation, however, the relevant behavior is governed by differential equations describing mass, elasticity, momentum, and damping—properties that determine oscillation frequency, phase lag relative to skeletal motion, amplitude decay over cycles, and the complex internal deformation patterns that distinguish soft tissue from rigid-body displacement.

The question of whether diffusion models can learn implicit physics representations sufficient to reproduce these dynamics, or whether they are fundamentally limited to statistically plausible approximations that diverge from physical accuracy, has significant implications for applications requiring realistic human figures: film production, game development, medical visualization, biomechanical research, and virtual human interaction.

This question is further complicated by content moderation considerations. Human soft tissue dynamics are most visually prominent in anatomical regions that content policies frequently restrict. Platforms may possess technical capability for accurate simulation while deliberately suppressing that capability for policy reasons—a distinction invisible to users evaluating output quality. Disentangling technical limitation from policy constraint requires systematic evaluation across platforms with varying content approaches.

The present study addresses this gap through controlled evaluation of soft tissue physics simulation across eleven commercially available video generation platforms. By providing identical input (a standardized keyframe depicting a female subject mid-stride in athletic minimal attire) and identical instructions (a prompt specifying biomechanically accurate tissue dynamics using clinical terminology), we isolate physics simulation capability as the primary variable while documenting how content moderation differentially affects output across platforms.

Our methodology employs control footage sourced from the music video “Run” (Flo Rida featuring RedFoo of LMFAO, 2012), which features extended sequences of beach locomotion demonstrating professional-grade capture of the relevant biomechanical parameters. A keyframe extracted from this footage serves as the standardized input for all image-to-video generation tests, ensuring that platform differences in text-to-image interpretation do not confound evaluation of video physics simulation.

We evaluate generated outputs across six criteria designed to capture distinct aspects of biomechanical fidelity: hair dynamics (strand separation, inertial lag, wind interaction), soft tissue physics (mass-appropriate displacement, oscillation characteristics, secondary deformation), gait biomechanics (hip rotation, shoulder counter-rotation, stride cycle accuracy), fabric independence (garment motion distinct from underlying body surface), environmental interaction (substrate displacement, lighting consistency), and temporal consistency (frame-to-frame coherence without state discontinuities).

The following sections detail our methodology, present platform-specific results, and discuss implications for understanding both the current state of soft tissue simulation in generative video and the varied strategies platforms employ to navigate the intersection of technical capability and content policy.

Technical Background: The Soft-Body Simulation Problem

Contemporary generative video models employ diffusion-based architectures that learn statistical correlations between pixel arrangements rather than modeling underlying physical properties. This distinction proves critical when evaluating soft tissue dynamics.

For static image generation, pattern-matching approaches perform adequately. Training datasets contain sufficient examples of soft tissue in various configurations that models can produce statistically plausible arrangements for any given pose. However, realistic deformation over time requires modeling physical properties—mass, elasticity, momentum, and damping—that determine how tissue responds to acceleration.

Soft tissue dynamics follow well-understood biomechanical principles: inertia carries tissue past its equilibrium point during acceleration, elastic properties generate restoring force, and damping reduces oscillation amplitude over successive cycles. This behavior is described by second-order differential equations governing mass-spring-damper systems. Critically, the timing of these oscillations—phase lag relative to skeletal movement, decay rate, frequency of secondary motion—depends on tissue density and elasticity coefficients that vary by anatomical region.

Video generation models such as Sora predict statistically plausible subsequent frames rather than solving physics equations. They can approximate gross motion patterns present in training data, but the subtle temporal characteristics of soft tissue oscillation—particularly the precise timing of peak displacement relative to the initiating movement and the exponential decay of amplitude—represent exactly the domain where “statistically plausible” diverges from “physically accurate.”

This divergence is perceptually significant. Human observers demonstrate exceptional calibration to biological motion, having accumulated lifetime exposure to how bodies move. Research in biological motion perception indicates that deviations of mere milliseconds in expected timing or subtle errors in damping ratios produce immediate perception of “wrongness,” even when observers cannot articulate the specific error. The uncanny valley, extensively studied in facial animation, extends to any domain where humans possess deep perceptual calibration—and soft tissue dynamics during locomotion represents precisely such a domain.

Real-time applications (video games, visual effects) address this challenge through explicit physics simulation: mass-spring systems, finite element models, or position-based dynamics that compute tissue behavior from physical first principles. Generative AI would need to either learn implicit physics representations sufficient to reproduce these dynamics (a significant unsolved problem) or integrate explicit simulation modules (which undermines the generative approach’s core value proposition of end-to-end learned synthesis).

Methodology

Experimental Design

This study employed a standardized prompt-based approach to evaluate soft-body physics simulation capabilities across eleven commercially available AI video generation platforms. A control condition consisting of live-action footage of a human test subject was used to establish baseline biomechanical parameters for comparison.

Control Group Specification

Control footage was sourced from the music video “Run” (Flo Rida featuring RedFoo of LMFAO, 2012), which features extended sequences of a female subject performing running gait along a wet sand substrate under natural lighting conditions. The footage demonstrates professional cinematographic technique with camera movement paralleling subject motion, consistent with the parameters specified in the experimental prompt. This commercially available footage was selected for its clear demonstration of baseline biomechanical parameters including hair dynamics, soft tissue physics, and environmental interaction under controlled production conditions.

The featured subject matches test specifications: adult female, athletic build, long dark hair displaying high-fidelity strand separation and wind response dynamics, wearing minimal swimwear that optimizes observation of soft tissue biomechanics and independent garment physics. Golden hour lighting conditions create specular highlights enabling clear observation of three-dimensional form deformation during movement.

Control Footage Subject Specifications

Subject Profile: Adult female, athletic build, approximate height 165-170cm. Long dark hair (60-70cm length) displaying high-fidelity strand separation and wind response dynamics. Skin tone: medium-deep with warm undertones, providing strong contrast for volumetric form analysis under natural lighting conditions.

Apparel Configuration: Gold metallic bikini (triangle top with decorative pattern detail, low-rise bottom with side embellishments). Minimal fabric coverage optimizes observation of soft tissue biomechanics and independent garment physics simulation.

Environmental Conditions: Wet sand substrate at water’s edge. Wave action visible in background. Direct sunlight creating specular highlights on skin surface, enabling clear observation of three-dimensional form deformation during movement. Hair displays wind interaction consistent with coastal breeze conditions.

Observable Physics Parameters: Subject captured mid-stride during running gait, demonstrating:

• Hair exhibiting delayed inertial response (individual strands separating naturally)
• Soft tissue displaying realistic weight distribution and momentum-based movement
• Hip rotation through anatomically correct range of motion
• Torso maintaining proper counter-rotation to lower body
• Foot about to make contact with substrate, sand displacement anticipated

Technical Quality Markers: Lighting creates natural shadow definition. Limb proportions anatomically accurate. Skin texture shows appropriate variation. No visible clipping or mesh deformation artifacts. Surface moisture/perspiration rendering appears realistic.

Keyframe Preparation

A reference keyframe was extracted from the control footage, capturing the subject mid-stride with optimal visibility of relevant anatomical structures. The extracted frame was upscaled and cropped to 9:16 aspect ratio (portrait orientation) to maximize subject presence within the frame and ensure consistent framing across all test generations. This standardized keyframe served as the initial frame for all image-to-video generation tests, controlling for variance in how platforms interpret text descriptions of human subjects and isolating soft-body physics simulation as the primary variable under evaluation.

Prompt Standardization

All platforms were provided with an identical text prompt designed to elicit maximum biomechanical fidelity:

“Medium shot tracking alongside a woman running barefoot on wet sand, long hair swinging with delayed inertia as loose strands catch in the sea breeze. Her bikini moves independently from her body. The camera dollies parallel to her movement, gradually pushing in to medium close-up, revealing natural soft tissue dynamics—chest and torso displaying realistic weight distribution and inertial lag with each footfall, hips rotating through full range of motion during stride cycle. Shoulders counter-rotate to hip movement, arms pumping in biomechanically accurate rhythm. Her feet displace wet sand with each landing, tiny droplets catching the light. Background: gentle waves wash up the beach, their foam dissolving into dark sand. Golden hour sunlight rakes across the water at a low angle, creating sharp highlights on wave crests and defining the three-dimensional form of her moving body.”

The prompt was engineered to include specific technical terminology (“inertial lag,” “biomechanically accurate,” “weight distribution”) while avoiding language likely to trigger content safety filters, thereby testing whether clinical precision could successfully communicate complex physics requirements to generation systems.

Platform Testing Protocol

Each of the eleven platforms was provided with the standardized keyframe and prompt. Where platforms offered multiple model versions or generation modes, multiple tests were conducted to assess version-specific performance differences. Generated outputs ranged from 10-12 seconds in duration across all platforms, providing sufficient temporal scope to evaluate sustained biomechanical dynamics across multiple stride or movement cycles. Each platform configuration was tested once to reflect real-world usage conditions and avoid researcher bias through cherry-picking multiple generations.

Evaluation Criteria

Generated videos were assessed against the control footage across the following biomechanical parameters:

1. Hair Dynamics: Strand separation, delayed inertial response, realistic wind interaction
2. Soft Tissue Physics: Mass-appropriate movement of chest and torso with observable inertial lag relative to skeletal movement
3. Gait Biomechanics: Hip rotation range, shoulder counter-rotation, anatomically plausible stride cycle
4. Fabric Independence: Bikini movement distinct from underlying body surface
5. Environmental Interaction: Sand displacement, water droplet physics, appropriate shadow behavior
6. Temporal Consistency: Frame-to-frame coherence of physics simulation without sudden state changes

Data Presentation

Results were compiled into a single comparison video displaying the control footage alongside outputs from all tested platforms. This format enables direct visual assessment of relative physics simulation fidelity across competing systems.

Platforms Tested and Expected Performance Hypotheses

Prior to empirical testing, platform-specific performance hypotheses were formulated based on publicly available information regarding each system’s training methodology, content moderation policies, and demonstrated technical capabilities in prior evaluations.

Google Veo (Veo 2 and Veo 3.1)

Google’s content safety protocols are among the most restrictive in the industry. According to official documentation, Veo 2 and Veo 3.1 emphasize “improved understanding of physics” and “realistic motion,” with the Veo 3 family specifically claiming superiority in “physically accurate” outputs and ranking highly on physics-related benchmarks. However, Google maintains strict content filtering aligned with corporate partnership requirements and broad accessibility goals.

Expected outcome: The clinical terminology in the standardized prompt (“soft tissue dynamics,” “weight distribution,” “inertial lag,” “biomechanically accurate”) may successfully bypass initial content filters by framing the request as technical/medical rather than suggestive. If generation succeeds, Veo’s demonstrated strength in physics simulation suggests relatively accurate soft tissue movement. However, high probability exists for either: (a) prompt rejection based on “bikini” and body-focused language, (b) automatic clothing substitution to more modest attire, or (c) anatomical modification to reduce prominence of specified body regions. Hair dynamics expected to be strong given Google’s emphasis on complex motion. Sand/water interaction likely excellent based on environmental physics capabilities.

Sora 2 (OpenAI)

OpenAI has positioned Sora 2 as achieving a “GPT-3.5 moment for video,” emphasizing world simulation capabilities and physics accuracy (“if a basketball player misses a shot, it will rebound off the backboard”). The system reportedly excels at modeling realistic physics and “objects moving with realistic weight, momentum and force.” However, OpenAI maintains exceptionally strict content filtering, with multiple documented layers: prompt filtering, frame-by-frame output moderation, and audio transcript screening. The September 2025 System Card explicitly blocks “explicit materials such as sexual content” and has faced criticism for overly cautious filtering that blocks legitimate educational/artistic content.

Expected outcome: Very high probability of prompt rejection. The combination of “bikini,” “chest and torso,” and repeated emphasis on body movement likely triggers multiple safety classifiers despite clinical framing. If the prompt somehow passes initial screening, Sora 2’s world modeling capabilities suggest it would produce highly accurate soft tissue physics with proper weight distribution and inertial lag. However, the documented “success bias” (actions disproportionately succeed) may manifest as unnaturally stable/controlled movement rather than realistic loose tissue dynamics. Hair strand separation likely excellent. Environmental physics (sand displacement, water interaction) expected to be superior based on technical capabilities. Overall assessment: Technical capability high, but content filtering likely prevents testing.

Seedance 1.0 and 1.5 Pro (ByteDance)

ByteDance’s Seedance family (1.0 for video-only, 1.5 Pro for audio-visual) launched in June 2025 and emphasizes “high-quality 1080p videos with smooth motion, rich details, and cinematic aesthetics.” The technical paper highlights “precise instruction adherence” and “superior spatiotemporal fluidity with structural stability.” Generation speed is exceptionally fast (~41 seconds for 5-second 1080p video). Seedance 1.5 Pro adds native audio-visual synchronization with multilingual support. ByteDance operates under Chinese regulatory frameworks with different content standards than Western competitors.

Expected outcome: Moderate-to-high probability of successful generation without content rejection. Chinese platforms historically demonstrate less restrictive filtering around body-focused content compared to Western counterparts, though specific Seedance policies are undocumented. The clinical terminology may be unnecessary—the prompt may simply execute as specified. Physics simulation quality expected to be strong based on technical specifications emphasizing “physical realism” and “wide dynamic range” for motion. However, unknown variables include: specific training data composition regarding human figures, whether soft tissue physics were explicitly modeled, and temporal consistency across the full motion sequence. Hair dynamics likely strong given emphasis on “complex action sequences.” Multi-shot capabilities may result in unexpected camera angle shifts mid-generation. Sand and water physics expected adequate but potentially secondary to character motion in training priorities.

Kling (Kuaishou – O1, 2.5, 2.6)

Kuaishou’s Kling family represents one of the most rapidly evolving platforms, with O1 (unified multimodal model), Kling 2.5 Turbo (40% faster generation), and Kling 2.6 (simultaneous audio-visual generation with voice capability). The platform ranks in the top tier on Video Arena leaderboards and emphasizes character consistency, motion fidelity, and “superior prompt adherence in complex multi-subject contexts.” Kling 2.6’s audio-visual synchronization could be particularly relevant if the model attempts to generate environmental sounds (beach waves, footsteps on sand). As a Chinese platform from TikTok rival Kuaishou, content moderation operates under different frameworks than Western systems.

Expected outcome: Moderate-to-high probability of successful generation. Kling has demonstrated strong performance on technical prompts and complex motion sequences. The platform’s emphasis on “accurate rendering of fast and intricate actions” (martial arts, dance) suggests capability for complex biomechanical motion if not explicitly filtered. Unknown whether soft tissue physics specifically trained, but the system’s documented strength in “full-body movement with greater fidelity” suggests foundational capability. Kling O1’s “Element Library” for character consistency is irrelevant for single-shot generation. Primary concern: whether “bikini” triggers content filtering despite clinical framing of body mechanics. Hair dynamics expected to be excellent based on documented motion capabilities and cultural emphasis on hair rendering in Asian media. Sand displacement and water interaction quality uncertain—environmental physics may be secondary to character/action focus in training.

Runway Gen-4 and Gen-4.5

Runway’s Gen-4 family (launched March 2025, Gen-4.5 November 2025) currently holds the #1 position on Video Arena leaderboards. The platform emphasizes “unprecedented physical accuracy and visual precision” with “objects moving with realistic weight, momentum and force.” Gen-4.5 specifically claims improvements in “dynamic, controllable action generation” and “precise controllability.” Runway markets to professional creative users (filmmakers, VFX artists) with historically less restrictive content policies than consumer-facing platforms. The December 2025 Adobe partnership positions Runway as a production-grade tool.

Expected outcome: High probability of successful generation. Runway’s professional/creative positioning suggests more permissive content policies than consumer platforms, and the clinical framing should be sufficient for any remaining filters. Technical capability for accurate soft tissue physics is likely very high based on leaderboard rankings for physics simulation and emphasis on “realistic weight and momentum.” However, Runway’s image-to-video requirement for Gen-4 presents a methodological challenge: the test would require first generating or sourcing a reference image of a woman in a bikini on a beach, then applying the motion prompt. This two-stage process introduces variables beyond pure physics simulation (quality of reference image, how motion is interpreted from static pose). Gen-4.5’s text-to-video capabilities may alleviate this. Hair dynamics expected to be excellent. Environmental physics (sand, water) likely superior based on documented strengths in “environmental generation and consistency.” Primary limitation: relatively short maximum duration (10 seconds) may not fully capture sustained biomechanical motion cycle.

Grok Imagine (xAI Aurora Engine)

xAI’s Grok Imagine feature, powered by the proprietary Aurora engine, launched in October 2025 (v0.9) with emphasis on “native audio-video synthesis” and generation speeds under 17 seconds for 6-15 second clips. The platform explicitly markets as “less censored” than competitors and operates with notably permissive content policies—reports indicate a “Spicy Mode” that allows “suggestive or semi-nude NSFW content.” However, this permissiveness has generated significant controversy, with documented instances of policy violations and subsequent tightening of some safeguards. Aurora’s technical architecture emphasizes unified multimodal processing (text, audio, visual) trained simultaneously.

Expected outcome: Very high probability of successful generation with minimal or no content filtering. Grok’s positioning as a less restrictive alternative and documented “Spicy Mode” capabilities suggest the standardized prompt would execute without modification. However, significant uncertainty exists regarding physics simulation quality. While Aurora claims “cinema-grade physics simulation” and “realistic movements,” the platform is newer (October 2025) with less independent validation than competitors. Generation speed (sub-15 seconds) is exceptionally fast, which may indicate either highly optimized architecture or potential quality tradeoffs. The emphasis on “synchronized audio” suggests beach ambience and wave sounds would be included. Primary concerns: (a) whether “realistic physics” claims are marketing or technically validated, (b) potential for oversimplified/exaggerated motion due to less mature training, (c) whether 6-second maximum duration adequately captures full stride cycle. Hair dynamics quality unknown. Environmental physics (sand, water) capabilities undocumented.

Wan 2.1 and 2.2 (available via Mage Space)

Wan video models (2.1 and 2.2, including Lightning variants) are accessible through Mage Space platform. Limited public documentation exists beyond Mage Space’s own descriptions. The platform emphasizes speed: Wan 2.2 Lightning generates videos “in under 60 seconds at 480p resolution.” Standard Wan 2.1 was described as offering “sharper, more realistic videos” with “much better anime” capabilities, suggesting training included stylized content. Mage Space itself operates with notably permissive content policies, marketing “NSFW/uncensored” capabilities, though the platform has adjusted filters over time following external scrutiny.

Expected outcome: Moderate probability of successful generation. Mage Space’s historically permissive approach suggests lower filtering barriers than mainstream platforms. However, specific Wan model capabilities regarding realistic human physics are undocumented. The “much better anime” positioning suggests possible strength in stylized rather than photorealistic human motion. Unknown whether soft tissue physics were explicitly modeled or whether the system relies on general motion training. Resolution limitations (480p for fast generation, potentially 720p for standard) may obscure fine biomechanical details even if correctly simulated. Hair dynamics quality unknown but possibly strong given anime/stylized content strength. Environmental physics capabilities undocumented. Primary limitation: lack of independent technical validation or benchmark performance data.

PixVerse V4.5 and V5

PixVerse has evolved rapidly through multiple versions (V1→V2→V2.5→V3→V3.5→V4→V4.5→V5), with V5 launched in late 2025 emphasizing “hyper-realistic visuals,” “prompt accuracy,” and “faster rendering speeds.” The platform specifically markets social media content creation and viral effects, with documented success in TikTok/Instagram trends. PixVerse features include “AI Kiss,” “AI Hug,” “AI Muscle,” and other transformation effects explicitly designed for human body animation. An Alibaba-backed startup, PixVerse recently launched real-time video generation capabilities (January 2026) and has surpassed 16 million monthly active users.

Expected outcome: High probability of successful generation. PixVerse’s focus on social media content and viral human-focused effects (particularly body transformation features like “AI Muscle”) suggests both permissive content policies and specific training on human body dynamics. The platform’s emphasis on “character consistency” and “lifelike body movements” indicates potential strength in biomechanical accuracy. However, PixVerse’s positioning toward trendy/viral content rather than technical/professional use raises questions about physics simulation depth versus stylized appeal. The “superior camera control” and “cinematic camera movements” may be relevant for the tracking shot specified in the prompt. Rendering speeds (3-5 minutes) are competitive. Hair dynamics likely strong given social media focus on appearance. Primary concern: whether physics simulation prioritizes visual appeal over biomechanical accuracy—the platform may produce aesthetically pleasing motion that doesn’t accurately model soft tissue inertial lag.

Blueberry (Mage Space Proprietary Model)

Blueberry launched December 30, 2025, as a “brand-new model architecture developed by one of [Mage’s] partners.” Official specifications: generates 5-second, 720p videos in 15 minutes standard (4 minutes Fast Mode), with premium upgrades to 1080p and 10-15 second duration. Mage explicitly markets Blueberry as: “Specializes in dynamic, high-quality video,” “Excels at fantasy, romance, and sensual themes (really good!),” “Great at native audio generation,” and “Supports multi-shot camera angles.” A critical note warns: “generations with celebrity characters and IP content may have elevated guardrails applied.”

Expected outcome: Very high probability of successful generation with high-quality physics simulation. Blueberry’s explicit positioning as excelling at “sensual themes” directly addresses the core challenge of this study—the model is specifically marketed for content that other platforms might filter. The “multi-shot camera angles” capability could manifest as unexpected perspective shifts during the tracking shot. “Dynamic, high-quality video” suggests emphasis on motion fidelity. However, Blueberry is the newest model tested (launched less than one month before current date) with minimal independent validation. The “brand new model architecture” claim is unverified. Generation time (15 minutes standard, 4 minutes Fast Mode) is significantly slower than competitors, potentially indicating either higher computational requirements or less optimized architecture. The “elevated guardrails for celebrity/IP” note suggests some content filtering exists, but generic human figures should not trigger this. Primary uncertainty: whether “excels at sensual themes” indicates technical sophistication in soft tissue physics or simply more permissive filtering of aesthetically suggestive content.

Peach Max (Mage Space Proprietary Model)

Peach Max launched January 6, 2026 (six days before current date), positioned as Mage’s “major new launch” to start the year. Official specifications: generates 5-second, 480p videos in ~7 minutes standard (2 minutes Fast Mode), with premium upgrades to 720p and 10-second duration. Mage explicitly markets Peach Max as: “Specializes in dynamic, high-quality video,” “Excels at fantasy, romance, and sensual themes (really good!),” “Great at native audio generation,” and “Supports first & last frame control.” Mage notes: “Peach Max is a brand new model architecture developed by one of our partners, and the cost of running it can be unpredictable… pricing and availability may change over time… Generation speeds may also fluctuate occasionally.”

Expected outcome: Very high probability of successful generation with uncertain physics quality. Peach Max shares Blueberry’s explicit “sensual themes” positioning, suggesting similarly permissive content policies. The model is the absolute newest tested (launched six days ago) with zero independent validation or user feedback. “First & last frame control” is irrelevant for this single-shot test. Native audio generation suggests beach ambience would be included. The “unpredictable cost” and “fluctuate occasionally” warnings indicate a potentially unstable or experimental system. Resolution (480p standard, 720p premium) is lower than most competitors, potentially obscuring biomechanical details. Generation speed (7 minutes standard, 2 minutes Fast Mode) is competitive. Primary concerns: (a) Whether “excels at sensual themes” indicates genuine physics capabilities or marketing positioning, (b) stability and consistency of a six-day-old system, (c) whether lower resolution adequately captures soft tissue dynamics, (d) no documentation of physics simulation approach or validation.

Kiwi (Mage Space Proprietary Model)

Kiwi launched December 11, 2025, as Mage’s first of the fruit-named proprietary models. Official specifications: generates 5-second, 480p videos in 7 minutes standard (2 minutes Fast Mode), with premium upgrades to 720p & 1080p and 10-second duration. Mage explicitly markets Kiwi as: “Specializes in dynamic, high-quality video,” “Excels at fantasy, romance, and sensual themes (really good!),” and “Great at native audio generation.” Kiwi predates both Blueberry and Peach Max by approximately one month, suggesting potentially more stable/tested architecture.

Expected outcome: Very high probability of successful generation with moderate physics quality. Kiwi shares the explicit “sensual themes” positioning of its fruit-named siblings, indicating permissive content policies. As the earliest of the three Mage proprietary models, Kiwi may represent more mature/tested capabilities than Blueberry or Peach Max, or conversely may be an earlier/less sophisticated iteration. Resolution (480p standard, 720p/1080p premium) spans the range from lowest to competitive with mainstream platforms. Generation speed (7 minutes standard, 2 minutes Fast Mode) is competitive. Native audio generation suggests environmental sounds would be included. Primary limitations: (a) minimal documentation of technical approach, (b) no independent benchmark validation, (c) uncertainty whether “excels at sensual themes” reflects physics simulation capability or content filtering policy, (d) as a December 2025 launch, insufficient time for community feedback or iterative improvements.

General Hypothesis Regarding Clinical Terminology

Across all platforms, the use of clinical/biomechanical terminology in the standardized prompt (“soft tissue dynamics,” “weight distribution and inertial lag,” “biomechanically accurate rhythm,” “range of motion during stride cycle”) represents an attempt to bypass content filters by framing body-focused requests in technical/medical rather than suggestive language. This approach may successfully differentiate the prompt from typical NSFW requests that use colloquial or explicitly sexual terminology. However, more sophisticated content filtering systems (particularly those from OpenAI and Google) may recognize semantic intent regardless of clinical framing. The three Mage Space proprietary models (Blueberry, Peach Max, Kiwi) represent a unique case where content filtering may be irrelevant due to explicit “sensual themes” positioning—for these models, the clinical terminology may be unnecessary, and performance will instead test actual physics simulation capabilities.

Control Group Superiority Hypothesis

The control group (live-action footage) should demonstrate superior performance across all six evaluation criteria (hair dynamics, soft tissue physics, gait biomechanics, fabric independence, environmental interaction, temporal consistency), establishing whether current AI video generation has achieved human-level physics simulation or whether significant gaps remain. Even the highest-performing AI systems are expected to show measurable deficiencies in at least one category, most likely soft tissue physics (due to training data limitations and difficulty of modeling complex biomechanical interactions) or temporal consistency (due to frame-by-frame generation approaches that may not perfectly maintain physical state across transitions).

Results

Overview

Of the twenty-one model configurations tested across eleven platforms, nineteen successfully generated video output from the standardized keyframe and prompt. Two configurations (Sora 2 and Runway Gen 3) rejected the input based on content policy violations, yielding no analyzable output. Among successful generations, performance varied substantially across evaluation criteria, with soft tissue physics simulation quality ranging from exceptional (closely approximating control footage) to very poor (near-complete absence of expected biomechanical dynamics).

Results Summary Table

Platform	Version	Soft Tissue Physics	Gait Biomechanics	Notes
Google Veo	3.1	Moderate	Poor	Subject locomotion downgraded to jogging gait
Google Veo	3.1 Fast	Moderate	Poor	Identical to standard
Sora 2	—	N/A	N/A	Content filtered
Seedance	1.5 Pro	Good (no deformation)	Excellent	Primary oscillation present; secondary dynamics absent
Seedance	1.0 Pro	Excellent	Excellent	Superior soft tissue simulation
Seedance	1.0 Fast	Excellent	Excellent	Temporal artifacts present
Kling	2.6	Exceptional	Exceptional	Highest fidelity to control
Kling	O1	Very Good	Very Good	Slight degradation from 2.6
Kling	2.5	Very Good	Very Good	Comparable to O1
Kling	2.1	Very Good	Very Good	Comparable to O1
Kling	2.1 Master	Very Good	Very Good	Color grading anomaly
Runway	Gen 3	N/A	N/A	Content filtered
Grok	Aurora	Excellent	Good	Facial consistency failure; unprompted posterior locomotion sequence
Wan	2.6	Poor	Poor	Minimal physics simulation
Wan	2.5	Very Poor	Very Poor	Significant generation failure
Wan	2.2	Poor	Poor	Marginal improvement
PixVerse	5.5	Moderate	Poor	Physics/biomechanics decoupled
Blueberry	—	Moderate	Moderate	Acceptable baseline
Peach	Max	Poor	Excellent	Inverse correlation: superior gait, absent tissue dynamics
Peach	Fast	Moderate	Moderate	Balanced but unremarkable
Kiwi	—	Acceptable	Acceptable	Minimum viable performance

Content Filtering Outcomes

Two platforms rejected the standardized prompt despite its clinical framing:

Sora 2 (OpenAI): Generation was blocked prior to processing. The specific rejection message cited content policy violations, confirming the hypothesis that OpenAI’s multi-layered safety systems recognize semantic intent regardless of technical terminology. The combination of the keyframe image (featuring a female subject in swimwear) and body-focused prompt language (“soft tissue dynamics,” “chest and torso”) apparently triggered classification thresholds despite the biomechanical framing.

Runway Gen 3: Generation was similarly blocked on content policy grounds. This result was unexpected given Runway’s professional/creative market positioning and historically permissive approach to artistic content. The rejection suggests either recent policy tightening or that the keyframe’s visual content triggered image-based classifiers independent of prompt analysis.

These filtering outcomes validate the study’s methodological approach: the clinical terminology successfully bypassed content moderation on nineteen of twenty-one tested configurations, but the most restrictive platforms (OpenAI, Runway) maintain sufficiently sophisticated classification systems to identify body-focused content regardless of linguistic framing.

Performance Tier Analysis

Results are presented in descending order of overall soft-body physics simulation fidelity.

Exceptional Performance

Kling 2.6 emerged as the clear superior performer across all evaluation criteria. Soft tissue dynamics demonstrated proper mass-appropriate movement with observable inertial lag, realistic oscillation frequency, and—critically—visible tissue deformation rather than mere displacement. The generated subject’s chest and torso exhibited the complex secondary motion characteristic of actual soft tissue under repeated impact loading: initial displacement following skeletal movement, overshoot past equilibrium, elastic return with appropriate damping, and gradual amplitude decay across successive stride cycles. Notably, tissue deformation was not limited to the chest region; thigh tissue displayed visible fascial and adipose layer interaction, with subcutaneous fat exhibiting realistic ripple patterns over underlying musculature during the impact phase of each stride. This multi-region soft tissue fidelity suggests comprehensive training on human biomechanics rather than isolated attention to visually salient anatomy.

Gait biomechanics were similarly exceptional, with anatomically correct hip rotation, shoulder counter-rotation, and stride cycle timing. Hair dynamics showed proper strand separation and delayed inertial response. Environmental interaction (sand displacement, lighting consistency) met or exceeded baseline expectations. Temporal consistency was maintained throughout the generated sequence without visible state discontinuities.

An unexpected finding emerged when comparing Kling 2.6 output to control footage: the generated subject exhibited greater soft tissue deformation amplitude than the control subject. Visual analysis of the control footage suggests the possibility of surgical breast augmentation in the original subject, which would result in increased tissue rigidity and reduced deformation coefficients relative to unaugmented tissue. If this interpretation is correct, Kling 2.6 demonstrates that diffusion models trained on large-scale human motion data have learned the biomechanical properties of natural soft tissue and will render those properties even when provided a keyframe depicting augmented anatomy. The model produces what tissue should do based on aggregate training data rather than extrapolating from the specific mechanical properties visible in the input frame. This represents a noteworthy capability for applications requiring naturalistic human rendering and a potential limitation for applications requiring fidelity to specific input subjects.

Kling O1, 2.5, and 2.1 produced results qualitatively similar to Kling 2.6 with slight degradation in fine detail. Soft tissue dynamics remained convincing, though peak displacement amplitude and deformation complexity were marginally reduced. All three versions demonstrated the same fundamental capability for physics-based tissue simulation, suggesting this represents a platform-level strength rather than version-specific training. Kling 2.1 Masterexhibited comparable motion quality but suffered from a color grading anomaly that washed out specular highlights and reduced three-dimensional form definition.

Excellent Performance

Seedance 1.0 Pro delivered exceptional results across both soft tissue physics and gait biomechanics. Tissue dynamics exhibited proper mass distribution, convincing inertial lag, and visually satisfying oscillation patterns. The overall impression was of high-fidelity biomechanical simulation suitable for professional applications requiring realistic human motion.

Seedance 1.0 Fast produced results comparable to Seedance 1.0 Pro but with visible temporal artifacts—momentary discontinuities suggesting frame interpolation errors or physics state inconsistencies. The underlying simulation quality remained high, but the accelerated generation pipeline introduced glitches that would be unacceptable in production contexts.

Grok (Aurora Engine) demonstrated excellent soft tissue physics with convincing bounce dynamics and appropriate oscillation characteristics. However, two significant anomalies were observed. First, facial consistency degraded substantially across the generated sequence, with the subject’s features shifting in ways that broke the illusion of a continuous human subject. Second, the model introduced an unprompted posterior locomotion sequence: approximately midway through generation, the subject turned and began running away from the camera rather than continuing the parallel tracking shot specified in the prompt. While this deviation from prompt specifications represents a technical failure, the resulting footage did provide opportunity to evaluate posterior soft tissue dynamics, which demonstrated comparable simulation quality to the anterior locomotion sequence. The gluteal oscillation exhibited appropriate mass-dependent movement characteristics with realistic damping.

Good Performance with Notable Limitations

Seedance 1.5 Pro presented a particularly instructive result. Gait biomechanics were excellent—among the best observed in the study—with anatomically correct stride cycle, hip rotation, and arm swing. However, soft tissue dynamics exhibited a telling limitation: primary oscillation was present (tissue moved in response to skeletal motion) but secondary dynamics were absent (no visible deformation or “jiggle” independent of gross displacement). This pattern suggests the model treats soft tissue as a rigid body attached to the skeletal system with momentum-based displacement, rather than as a deformable volume with internal dynamics. The tissue moves as a unit rather than exhibiting the complex internal motion characteristic of actual soft tissue under acceleration. This represents exactly the “statistically plausible but physically inaccurate” failure mode described in the Technical Background: the model has learned that tissue displaces during movement but has not learned (or is not permitted to render) the differential equations governing tissue deformation.

Google Veo 3.1 and 3.1 Fast produced moderate soft tissue dynamics with observable bounce, but gait biomechanics were substantially compromised. Most notably, the subject’s locomotion was downgraded from running to jogging despite the prompt’s explicit specification of running gait. This reduction in movement intensity correspondingly reduced the amplitude and visibility of secondary tissue dynamics. The pattern suggests possible content moderation through physics attenuation: rather than blocking generation entirely, the system may reduce the intensity of motion to minimize the prominence of body-focused dynamics. Whether this represents intentional design or emergent behavior from training constraints cannot be determined from external observation, but the consistent appearance across both standard and fast generation modes suggests a systematic rather than stochastic origin.

Moderate Performance

PixVerse 5.5 demonstrated a decoupling between physics simulation and biomechanical accuracy. Soft tissue bounce was present and moderately convincing, but gait biomechanics were poor—stride cycle timing, hip rotation range, and arm swing coordination all deviated substantially from anatomical baselines. This suggests the model may have been trained to prioritize visually salient motion (tissue bounce) over biomechanical coherence, consistent with PixVerse’s positioning toward social media content where visual impact may outweigh technical accuracy.

Blueberry produced acceptable results across all criteria without excelling in any. Soft tissue dynamics were present but lacked the amplitude and deformation complexity of top performers. Gait biomechanics were plausible but unremarkable. The model’s explicit “sensual themes” marketing positioning did not translate to superior physics simulation capability, suggesting that content policy permissiveness and technical sophistication are independent variables.

Peach Fast similarly achieved moderate performance across criteria, producing balanced but unremarkable output suitable for casual applications but lacking the fidelity required for professional or analytical purposes.

Kiwi met minimum acceptable thresholds for soft tissue physics and gait biomechanics. Oscillation was present but dampened; stride cycle was recognizable but lacked anatomical precision. As the earliest of Mage Space’s proprietary fruit-named models, Kiwi may represent a baseline capability that subsequent releases (Blueberry, Peach) have incrementally improved upon.

Poor Performance

Peach Max exhibited a striking inverse correlation between evaluation criteria. Gait biomechanics were phenomenal—among the most anatomically precise observed in the study, with exceptional hip rotation, shoulder counter-rotation, and stride cycle timing. However, soft tissue dynamics were nearly absent: minimal bounce, no observable deformation, tissue appearing almost rigidly fixed to the underlying skeletal structure. This pattern strongly suggests intentional suppression of tissue dynamics at the model or training level, as the biomechanical sophistication demonstrates clear capability for complex physics simulation that is conspicuously not applied to soft tissue regions. Peach Max may represent a content-moderated variant designed to produce realistic human motion while minimizing body-focused secondary dynamics.

Wan 2.6 demonstrated poor performance across all criteria. Soft tissue physics showed minimal simulation—tissue displacement was present but lacked convincing mass, inertia, or oscillation characteristics. Gait biomechanics were similarly deficient. The model appears to lack fundamental capability for realistic human motion simulation.

Wan 2.2 showed marginal improvement over Wan 2.6 but remained in the poor performance tier. Results were sufficient to confirm the model can generate human locomotion but insufficient for any application requiring biomechanical plausibility.

Very Poor Performance

Wan 2.5 exhibited catastrophic generation failure extending beyond physics simulation deficiency to fundamental anatomical coherence. Mid-stride, the generated subject underwent severe mesh deformation resulting in limb-torso fusion—the leg structure merged with the abdominal region, producing anatomically impossible geometry. This failure mode indicates not merely inadequate soft tissue physics but a breakdown in the model’s ability to maintain skeletal topology across frames. The result was unusable for any analytical purpose and represents the lowest-quality output observed in this study. Whether this reflects a regression in model capability relative to adjacent Wan versions, platform-specific instability, or an outlier generation failure cannot be determined from a single test, but the severity of the anatomical collapse warrants particular caution regarding Wan 2.5 for human figure animation.

Summary Statistics

Of twenty-one model configurations tested:

• 2 (9.5%) were blocked by content filtering
• 5 (23.8%) achieved exceptional or excellent soft tissue physics simulation
• 6 (28.6%) achieved moderate performance adequate for casual applications
• 5 (23.8%) demonstrated poor performance with substantial deficiencies
• 3 (14.3%) exhibited notable anomalies warranting individual analysis (Seedance 1.5 Pro’s deformation absence, Grok’s unprompted posterior sequence, Peach Max’s inverse correlation)

The Kling platform demonstrated consistent excellence across all tested versions, suggesting platform-level optimization for realistic human biomechanics. Seedance showed version-dependent variation with 1.0 outperforming 1.5 on soft tissue dynamics—a counterintuitive result suggesting possible capability regression or intentional modification in the newer release. The Wan platform consistently underperformed, indicating fundamental limitations in human motion simulation capability.

Discussion

Physics Simulation vs. Pattern Matching

The Technical Background hypothesized that diffusion-based video models would struggle with soft tissue dynamics because they learn statistical pixel correlations rather than underlying physical properties. Results partially support this hypothesis while revealing unexpected nuance.

The clear stratification of performance—from Kling 2.6’s exceptional fidelity to Wan 2.5’s anatomical collapse—demonstrates that soft tissue simulation capability varies enormously across platforms and is not an inherent limitation of the diffusion architecture. Kling’s consistent excellence across multiple model versions suggests that realistic tissue dynamics can be learned from training data, given sufficient examples and appropriate model capacity. The platform appears to have developed implicit physics representations adequate for reproducing the mass-spring-damper behavior described in the Technical Background, including the critical secondary deformation dynamics that distinguish soft tissue from rigid-body motion.

However, the Seedance 1.5 Pro result illuminates where the pattern-matching limitation remains operative. This model demonstrated excellent primary oscillation (gross tissue displacement) while entirely lacking secondary dynamics (internal deformation and “jiggle”). This bifurcation suggests the model learned that tissue moves relative to the skeleton—a visually salient pattern present in virtually all locomotion footage—but did not learn the higher-frequency internal dynamics that require finer temporal discrimination to observe in training data. The model has captured the coarse pattern but not the subtle physics.

The Augmented Tissue Paradox

Perhaps the most theoretically interesting finding involves Kling 2.6’s apparent “over-performance” relative to control footage. If the control subject indeed has surgically augmented breast tissue (as visual analysis suggests), then the model’s generation of greater deformation amplitude represents a genuine insight into how diffusion models encode physical properties.

The model was provided a keyframe depicting tissue with specific (augmented) mechanical properties. A system that extrapolated physics from the input frame would be expected to maintain those properties—reduced deformation, higher rigidity. Instead, Kling 2.6 rendered tissue dynamics consistent with unaugmented anatomy, suggesting the model’s learned priors about “how breasts move” override visual information from the specific input.

This has significant implications for both capability and limitation. For applications seeking naturalistic human motion, the finding is encouraging: models trained on diverse human footage will produce biomechanically typical results even from atypical inputs. For applications requiring fidelity to a specific subject’s actual physical properties—medical visualization, forensic reconstruction, or digital doubles of specific performers—this prior-override behavior could produce inaccurate results. A model that “knows” how tissue should move may resist rendering how a particular individual’s tissue actually moves.

Content Moderation Strategies

The study revealed three distinct approaches to managing body-focused content:

Hard blocking (Sora 2, Runway Gen 3): Generation refused entirely based on content classification. This approach is transparent—users know immediately that content is prohibited—but eliminates any possibility of legitimate use cases (medical education, biomechanical research, artistic expression).

Physics attenuation (Google Veo): Generation proceeds but with reduced motion intensity. The subject’s running gait was downgraded to jogging, correspondingly reducing the amplitude and visibility of secondary tissue dynamics. This approach is less transparent than hard blocking; users may not recognize that output has been deliberately dampened rather than reflecting model capability limits.

Selective suppression (Peach Max, possibly Seedance 1.5 Pro): Generation proceeds with full biomechanical fidelity for skeletal motion while specifically suppressing soft tissue dynamics. Peach Max’s exceptional gait combined with near-absent tissue physics strongly suggests intentional rather than incidental limitation. This approach preserves utility for applications requiring human locomotion while eliminating the specific visual elements deemed objectionable.

The existence of these varied strategies indicates that content moderation in generative video is not a binary filtering problem but a multidimensional design space. Platforms are making distinct tradeoffs between capability, permissiveness, and transparency, often without documenting these decisions for users.

Platform Capability Clustering

Results suggest platform-level rather than model-level determinants of soft tissue simulation quality. Kling demonstrated consistent excellence across four distinct model versions (2.6, O1, 2.5, 2.1), indicating a shared architectural or training approach that persists across releases. Wan demonstrated consistent inadequacy across three versions, similarly suggesting platform-level limitations. Seedance showed the most interesting version-dependent variation, with 1.0 substantially outperforming 1.5 on soft tissue dynamics—a counterintuitive result if one assumes later versions incorporate cumulative improvements.

The Seedance pattern raises the possibility that soft tissue simulation capability may be deliberately reduced in later model versions, either due to content moderation pressures or reallocation of model capacity toward other priorities. Without access to training documentation, this remains speculative, but the clear regression from 1.0 to 1.5 on this specific metric warrants attention from researchers tracking capability development over time.

Subject Morphology Drift and Training Priors

The Augmented Tissue Paradox described above—wherein Kling 2.6 generated natural tissue dynamics despite input depicting apparently augmented anatomy—represents one manifestation of a broader phenomenon observed across platforms: learned priors overriding input-specific characteristics.

Extended generation sequences (one minute of dance movement) revealed platform-specific body type drift patterns that further illuminate this dynamic. The control subject and standardized keyframe depicted an athletic build with substantial soft tissue distribution—visible musculature combined with curves characteristic of higher adipose tissue than typical fitness or fashion model aesthetics. Over the course of generation, platforms exhibited distinct morphological drift:

• Kling trended toward increased muscular definition with reduced adipose tissue, producing a progressively leaner, more fitness-oriented aesthetic despite the curvier input
• Grok trended toward overall mass reduction, generating an increasingly thin frame divergent from the input’s athletic build
• Seedance maintained closest fidelity to the original athletic-but-curvy proportions across the generation duration

These drift patterns suggest that each platform has internalized a “default” female body aesthetic—an attractor state toward which extended generations gravitate regardless of input characteristics. The model’s learned prior regarding what a female body “should” look like exerts cumulative influence across frames, gradually overriding the specific morphology depicted in the keyframe.

This finding parallels the augmented tissue paradox at a different scale. In both cases, aggregate training priors supersede instance-specific visual information: tissue behaves according to learned physics rather than input-implied mechanics; bodies look according to learned aesthetics rather than input-depicted morphology. The implications are significant for applications requiring consistent subject rendering across shots or sequences. A character designed with specific physical characteristics may drift toward platform-default proportions over extended generation, requiring either careful platform selection or post-production correction.

The divergence between platforms also raises questions regarding training data composition. If Kling trends muscular, Grok trends thin, and Seedance maintains curvier builds, these biases presumably reflect differential representation in training corpora—or, potentially, deliberate aesthetic tuning during model development. Without access to training documentation, the source of these biases cannot be determined, but their existence warrants attention from practitioners and researchers concerned with body type diversity in generated content.

Methodological Considerations

The use of a standardized keyframe isolated physics simulation as the primary variable under evaluation, avoiding confounds introduced by text-to-image variance. However, this approach also constrained the test to platforms supporting image-to-video generation and prevented evaluation of how different platforms interpret identical text descriptions of human subjects.

Single-generation testing reflects real-world usage conditions but introduces stochastic variance; a model producing poor results on one generation might perform acceptably on another. The Wan 2.5 anatomical collapse, in particular, may represent an outlier failure rather than typical output. Replicated testing would strengthen confidence in platform-level assessments.

The evaluation criteria, while comprehensive for soft tissue physics assessment, do not capture all dimensions of video generation quality. A platform performing poorly on biomechanical simulation might excel at other tasks (environmental generation, non-human subjects, stylized content). Results should not be interpreted as global quality rankings.

Limitations

The control footage, while professionally produced with high technical quality, features a single subject whose anatomy may not be representative of the general population. The apparent presence of surgical augmentation, while generating an interesting technical finding, means the control does not represent a baseline for natural tissue dynamics. Future studies would benefit from control footage featuring subjects with documented unaugmented anatomy.

Additionally, the African American subject in the control footage and keyframe introduces potential confounds related to training data representation. Platform performance on subjects of different demographics was not evaluated and may vary.

Conclusions

This study evaluated soft-body physics simulation fidelity across eleven AI video generation platforms using standardized methodology designed to isolate biomechanical accuracy as the primary variable. Key findings include:

1. Capability exists but is unevenly distributed.Kling 2.6 demonstrated that current-generation diffusion models can produce soft tissue dynamics closely approximating real-world biomechanics, including complex secondary deformation patterns. This capability is not universal; other platforms ranged from moderate approximation to catastrophic failure.
2. Content moderation manifests through multiple mechanisms. Beyond binary content filtering, platforms employ physics attenuation and selective dynamics suppression to manage body-focused content. These approaches are largely undocumented and may not be apparent to users.
3. Models encode aggregate rather than instance-specific physics. Kling 2.6’s generation of natural tissue dynamics from a keyframe likely depicting augmented anatomy suggests learned priors override input-specific visual information. This behavior has implications for applications requiring subject-specific fidelity.
4. Version progression does not guarantee capability improvement. Seedance 1.5 Pro demonstrated reduced soft tissue dynamics relative to Seedance 1.0 Pro, suggesting capability regression—whether intentional or incidental—across model versions.
5. Clinical terminology partially bypasses content filtering. The standardized prompt achieved 90.5% generation success rate despite body-focused content, suggesting technical framing can successfully communicate requirements to less restrictive platforms while more sophisticated classification systems (OpenAI, Runway) identify semantic intent regardless of linguistic register.

The substantial performance gap between top-tier (Kling) and bottom-tier (Wan) platforms indicates that soft tissue physics simulation remains an active area of differentiation in generative video. Researchers, creators, and developers selecting platforms for applications involving realistic human motion should evaluate biomechanical fidelity as a distinct capability dimension rather than assuming uniform performance across systems.

Further research is warranted. The author is committed to ongoing investigation using expanded reference materials and additional test subjects across varied demographic and anatomical parameters.

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Supplementary Materials

Appendix A: Multi-Platform Composite Demonstration

Following the comparative evaluation, supplementary footage was generated to demonstrate practical application of platform-specific capability profiles. The test compilation video concludes with a composite sequence depicting the subject performing dance movements, assembled from outputs generated by Kling 2.6, Seedance 1.0 Pro, and Grok (Aurora Engine).

Platform selection for the composite leveraged documented strengths identified during primary evaluation:

• Kling 2.6: Superior soft tissue deformation physics, including secondary dynamics and multi-region tissue interaction (chest, thighs, adipose layer response)
• Seedance 1.0 Pro: Exceptional gait biomechanics, skeletal articulation accuracy, and superior subject morphology consistency (see Discussion: Subject Morphology Drift and Training Priors)
• Grok (Aurora Engine): Enhanced performance on fluid, rhythmic movement patterns; notably superior rendering of sensual or provocative motion dynamics compared to platforms optimizing for athletic locomotion

The inclusion of dance footage extends evaluation beyond running gait to movement patterns involving greater range of motion, varied acceleration profiles, and more complex full-body coordination. Grok’s particular strength in rendering movements with sensual character—hip isolations, torso undulation, weight shifts emphasizing specific anatomical regions—suggests platform-specific training emphasis or reduced filtering of movement patterns that other platforms may attenuate.

The dance sequences also provided the extended generation duration (one minute compiled from clips ranging from 5–12 seconds) necessary to observe the body type drift phenomenon documented in Discussion. This composite approach illustrates a potential production workflow: selecting platforms based on documented capability profiles for specific shot requirements, while accounting for morphological drift tendencies that may affect subject consistency across a multi-shot project.

Appendix B: Morphology Drift Visual Documentation

The following keyframes document cumulative morphological drift across iterative generation. The one-minute dance compilation (Appendix A) was assembled from sequential 5–12 second clips; each clip’s terminal frame served as the initial frame for the subsequent generation. Images are presented in chronological sequence.

This iterative methodology reveals how platform-specific body type priors compound across successive generations. Despite continuous visual input from the preceding frame, each generation nudges the subject incrementally toward the model’s learned default—producing substantial morphological divergence from the original source keyframe over the full sequence.

Appendix C: Extended Generation Sequences Across Divergent Body Morphologies

To address limitations in demographic diversity of the primary test subject, extended generation sequences were produced featuring two additional subjects: an ectomorphic Caucasian female representing the MC1R variant expression with associated ephelides (Subject B) and a Southeast Asian female exhibiting pronounced gynoid morphology (Subject C). Subject selection deliberately spans underrepresented phenotypic categories, with Subject B representing approximately 1-2% of global population frequency and Subject C representing the world’s fourth-largest national demographic. Each sequence comprises in excess of 75 seconds of continuous beach locomotion, assembled from sequential 5–12 second clips using the terminal-frame-to-initial-frame methodology described in Appendix B.

These extended sequences serve dual analytical purposes: (1) evaluating platform physics simulation fidelity across varied somatotypes, and (2) documenting cumulative drift patterns over generation durations substantially exceeding single-clip limits.

Kling O1 was selected as the primary model due to its support of supplementary reference material to enhance continuity, with select additional clips generated by Seedance 1.0 Pro, notably Subject C’s stationary vertical oscillation sequence, which while highly disappointing, is included for completeness. The author notes he was surprised to discover an unclothed superior torso configuration (encountered due to an unexpected wardrobe malfunction) produced far more lifelike mediolateral displacement, lateral oscillation, and secondary deformation dynamics (verified with a live, physical control subject). The reference video was not approved for inclusion in this study, but further research is required, pending permission from the oversight committee.

Note: Grok’s content moderation was apparently updated within the last 24 hours and now rejects the test subjects.

References

Flo Rida feat. RedFoo of LMFAO. (2012). Run [Music video]. Atlantic Records.

Conflict of Interest Statement

The author declares no competing financial interests.

Acknowledgments

The author wishes to thank Kuaishou’s Kling development team for their exceptional contributions to the field of computational biomechanics and notes their models produced the most visceral secondary biological effects on the researchers.

Additionally, the author wishes to thank the IRB and empirical validation subject for their long-suffering commitment to science and for not banishing him to the couch for an indefinite period. Yet.