title: Morph: A Motion-free Physics Optimization Framework for Human Motion
    Generation
summary: 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.
paper_authors._id: 674920a71d4a44cf36af83ca
paper_authors.name: Zhuo Li
paper_authors._id: 674920a71d4a44cf36af83cb
paper_authors.name: Mingshuang Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-29T16:44:29.643Z
paper_authors._id: 6178ea05267320abb99df778
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1636104707106-6178ea05267320abb99df778.jpeg
paper_authors.fullname: Mingshuang Luo
paper_authors.type: user
paper_authors.user: luomingshuang
paper_authors._id: 674920a71d4a44cf36af83cc
paper_authors.name: Ruibing Hou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-29T16:44:34.968Z
paper_authors._id: 64f1e1badd92641727444364
paper_authors.avatarUrl: /avatars/a2e9331b456694ff5f955281416e33d9.svg
paper_authors.fullname: ruibing hou
paper_authors.type: user
paper_authors.user: flow2023
paper_authors._id: 674920a71d4a44cf36af83cd
paper_authors.name: Xin Zhao
paper_authors._id: 674920a71d4a44cf36af83ce
paper_authors.name: Hao Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-29T16:44:53.985Z
paper_authors._id: 62aa53fc5df2251f7df798c6
paper_authors.avatarUrl: /avatars/1c7828ce861c30ddd0afceff355a92ec.svg
paper_authors.fullname: Hao
paper_authors.type: user
paper_authors.user: haoliu
paper_authors._id: 674920a71d4a44cf36af83cf
paper_authors.name: Hong Chang
paper_authors._id: 674920a71d4a44cf36af83d0
paper_authors.name: Zimo Liu
paper_authors._id: 674920a71d4a44cf36af83d1
paper_authors.name: Chen Li
paper_title: Morph: A Motion-free Physics Optimization Framework for Human Motion
    Generation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Motion Generator Motion Physics Refinement module synthetic motion data motion imitator physics simulator physically plausible space fine-tuning