标题： FlexMotion：轻量级、物理感知且可控的人体运动生成 显示英文标题

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

摘要： 轻量、可控且物理上合理的运动合成对于动画、虚拟现实、机器人和人机交互应用至关重要。 现有方法通常在计算效率、物理真实感或空间可控性之间做出权衡。 我们提出了FlexMotion，一种新颖的框架，该框架利用计算轻量级的扩散模型在潜在空间中运行，消除了对物理模拟器的需求，并实现了快速高效的训练。 FlexMotion采用多模态预训练Transformer编码器-解码器，结合关节位置、接触力、关节驱动和肌肉激活，以确保生成运动的物理合理性。 FlexMotion还引入了一个即插即用模块，可在一系列运动参数（例如关节位置、关节驱动、接触力和肌肉激活）上实现空间可控性。 我们的框架在提高效率和控制能力的同时实现了逼真的运动生成，为人类运动合成设定了新基准。 我们在扩展的数据集上评估了FlexMotion，并证明了其在真实性、物理合理性和可控性方面的优越性能。 显示英文摘要

摘要： 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.