Morph: A Motion-free Physics Optimization Framework for Human Motion
  Generation
Authors: name: Zhuo Li, name: Mingshuang Luo, name: Ruibing Hou, name: Xin Zhao, name: Hao Liu, name: Hong Chang, name: Zimo Liu, name: Chen Li

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.