BioMoDiffuse: Physics-Guided Biomechanical Diffusion for Controllable and Authentic Human Motion Synthesis

• URL: http://arxiv.org/abs/2503.06151v1
• Date: Sat, 08 Mar 2025 10:22:36 GMT
• Title: BioMoDiffuse: Physics-Guided Biomechanical Diffusion for Controllable and Authentic Human Motion Synthesis
• Authors: Zixi Kang, Xinghan Wang, Yadong Mu,
• Abstract summary: This paper introduces BioMoDiffuse, a novel biomechanics-aware diffusion framework.<n>It features three key innovations: (1) A lightweight biodynamic network that integrates muscle electromyography (EMG) signals with acceleration constraints; (2) A physics-guided diffusion process that incorporates real-time biomechanical verification via modified Euler-Lagrange equations; and (3) A decoupled control mechanism that allows independent regulation of motion speed and semantic context.
• Score: 21.750804738752105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human motion generation holds significant promise in fields such as animation, film production, and robotics. However, existing methods often fail to produce physically plausible movements that adhere to biomechanical principles. While recent autoregressive and diffusion models have improved visual quality, they frequently overlook essential biodynamic features, such as muscle activation patterns and joint coordination, leading to motions that either violate physical laws or lack controllability. This paper introduces BioMoDiffuse, a novel biomechanics-aware diffusion framework that addresses these limitations. It features three key innovations: (1) A lightweight biodynamic network that integrates muscle electromyography (EMG) signals and kinematic features with acceleration constraints, (2) A physics-guided diffusion process that incorporates real-time biomechanical verification via modified Euler-Lagrange equations, and (3) A decoupled control mechanism that allows independent regulation of motion speed and semantic context. We also propose a set of comprehensive evaluation protocols that combines traditional metrics (FID, R-precision, etc.) with new biomechanical criteria (smoothness, foot sliding, floating, etc.). Our approach bridges the gap between data-driven motion synthesis and biomechanical authenticity, establishing new benchmarks for physically accurate motion generation.

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