Morph：一种用于人体动作生成的无运动物理优化框架

Morph: A Motion-free Physics Optimization Framework for Human Motion Generation

November 22, 2024

作者: Zhuo Li, Mingshuang Luo, Ruibing Hou, Xin Zhao, Hao Liu, Hong Chang, Zimo Liu, Chen Li

cs.AI

摘要

人体动作生成在数字人和人形机器人控制等应用中起着至关重要的作用。然而，大多数现有方法忽略了物理约束，导致频繁产生具有明显缺陷的物理不合理动作，如漂浮和脚滑动。在本文中，我们提出了Morph，一个无运动物理的优化框架，包括一个运动生成器和一个运动物理细化模块，用于增强物理合理性，而不依赖昂贵的现实世界运动数据。具体而言，运动生成器负责提供大规模的合成运动数据，而运动物理细化模块利用这些合成数据在物理模拟器内训练一个运动模仿器，强制执行物理约束将嘈杂的动作投影到一个物理合理的空间。这些经过物理细化的动作进一步用于微调运动生成器，从而增强其能力。在文本到动作和音乐到舞蹈生成任务上的实验表明，我们的框架在改善物理合理性的同时实现了最先进的动作生成质量。

English

Human motion generation plays a vital role in applications such as digital humans and humanoid robot control. However, most existing approaches disregard physics constraints, leading to the frequent production of physically implausible motions with pronounced artifacts such as floating and foot sliding. In this paper, we propose Morph, a Motion-free physics optimization framework, comprising a Motion Generator and a Motion Physics Refinement module, for enhancing physical plausibility without relying on costly real-world motion data. Specifically, the Motion Generator is responsible for providing large-scale synthetic motion data, while the Motion Physics Refinement Module utilizes these synthetic data to train a motion imitator within a physics simulator, enforcing physical constraints to project the noisy motions into a physically-plausible space. These physically refined motions, in turn, are used to fine-tune the Motion Generator, further enhancing its capability. Experiments on both text-to-motion and music-to-dance generation tasks demonstrate that our framework achieves state-of-the-art motion generation quality while improving physical plausibility drastically.