轻量级物理感知人体动作生成，无需物理模拟即可高效可控。
FlexMotion: Lightweight, Physics-Aware, and Controllable Human Motion Generation
- 基于潜空间扩散模型，避免物理模拟提升效率。
- 融合关节位置、接触力等多模态信号，保证动作真实合理。
- 插件式模块支持多种运动参数的灵活控制，适合动画与交互场景。
轻量、可控制且物理合理的动作生成对动画、虚拟现实、机器人及人机交互至关重要。现有方法常在计算效率、物理真实性与空间可控性之间权衡。本文提出FlexMotion框架，采用计算轻量的潜空间扩散模型，无需物理模拟，实现快速高效训练。该模型使用多模态预训练Transformer编码器-解码器，整合关节位置、接触力、关节驱动力和肌肉激活信息，确保生成动作的物理合理性。同时引入即插即用模块，实现对关节位置、驱动力、接触力和肌肉激活等运动参数的灵活空间控制。在扩展数据集上的评估表明，该框架在动作真实性、物理合理性与可控性方面均表现优越，为人体动作合成设立了新基准。
原文摘要 · Abstract (English)
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.
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