FlexMotion: Lightweight, Physics-Aware, and Controllable Human Motion Generation

2025-01-28

AI Summary

Existing methods for human motion generation struggle to simultaneously achieve computational efficiency, physical plausibility, and precise spatial controllability—limiting their applicability in animation, VR, and robotics. This paper proposes a lightweight, physically grounded, and precisely controllable generative framework. First, it introduces a latent-space lightweight diffusion model as a computationally efficient alternative to traditional physics-based simulation. Second, it designs a pre-trained Transformer encoder-decoder that fuses multimodal biomechanical signals—including electromyography (EMG), ground contact forces, and joint dynamics—to encode rich motor priors. Third, it incorporates a plug-and-play parametric spatial control module enabling joint-level conditioning on target joint poses, contact forces, and actuation torques. Evaluated on an extended benchmark dataset, our method significantly improves motion fidelity, physical consistency, and controllability, while supporting efficient training and real-time inference—establishing a new state-of-the-art for interactive human motion synthesis.

Technology Category

Humans and AI: Human-Aware Planning and Behavior PredictionComputer Vision: Low Level & Physics-based VisionIntelligent Robots: Motion and Path Planning

Application Category

User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsResponsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Lightweight, controllable, and physically plausible human motion synthesis is crucial for animation, virtual reality, robotics, and human-computer interaction applications. Existing methods often compromise between computational efficiency, physical realism, or spatial controllability. We propose FlexMotion, a novel framework that leverages a computationally lightweight diffusion model operating in the latent space, eliminating the need for physics simulators and enabling fast and efficient training. FlexMotion employs a multimodal pre-trained Transformer encoder-decoder, integrating joint locations, contact forces, joint actuations and muscle activations to ensure the physical plausibility of the generated motions. FlexMotion also introduces a plug-and-play module, which adds spatial controllability over a range of motion parameters (e.g., joint locations, joint actuations, contact forces, and muscle activations). Our framework achieves realistic motion generation with improved efficiency and control, setting a new benchmark for human motion synthesis. We evaluate FlexMotion on extended datasets and demonstrate its superior performance in terms of realism, physical plausibility, and controllability.

Problem

• Human Motion Synthesis
• Computational Efficiency
• Realism and Control

Innovation

• FlexMotion
• Advanced Encoding Technique
• Efficient Computation Model