Morph: A Motion-free Physics Optimization Framework for Human Motion
  Generation
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.
674920a71d4a44cf36af83ca
Zhuo Li
674920a71d4a44cf36af83cb
Mingshuang Luo
admin_assigned
2024-11-29T16:44:29.643Z
6178ea05267320abb99df778
https://cdn-avatars.huggingface.co/v1/production/uploads/1636104707106-6178ea05267320abb99df778.jpeg
Mingshuang Luo
user
luomingshuang
674920a71d4a44cf36af83cc
Ruibing Hou
admin_assigned
2024-11-29T16:44:34.968Z
64f1e1badd92641727444364
/avatars/a2e9331b456694ff5f955281416e33d9.svg
ruibing hou
user
flow2023
674920a71d4a44cf36af83cd
Xin Zhao
674920a71d4a44cf36af83ce
Hao Liu
admin_assigned
2024-11-29T16:44:53.985Z
62aa53fc5df2251f7df798c6
/avatars/1c7828ce861c30ddd0afceff355a92ec.svg
Hao
user
haoliu
674920a71d4a44cf36af83cf
Hong Chang
674920a71d4a44cf36af83d0
Zimo Liu
674920a71d4a44cf36af83d1
Chen Li
Morph: A Motion-free Physics Optimization Framework for Human Motion
  Generation
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.
The Morph framework generates physically plausible human motions using a Motion Generator and a Motion Physics Refinement module, improving motion quality and physical accuracy in text-to-motion and music-to-dance tasks.
Motion Generator Motion Physics Refinement module synthetic motion data motion imitator physics simulator physically plausible space fine-tuning