title: Articulated Kinematics Distillation from Video Diffusion Models
summary: We present Articulated Kinematics Distillation (AKD), a framework for
  generating high-fidelity character animations by merging the strengths of
  skeleton-based animation and modern generative models. AKD uses a
  skeleton-based representation for rigged 3D assets, drastically reducing the
  Degrees of Freedom (DoFs) by focusing on joint-level control, which allows for
  efficient, consistent motion synthesis. Through Score Distillation Sampling
  (SDS) with pre-trained video diffusion models, AKD distills complex,
  articulated motions while maintaining structural integrity, overcoming
  challenges faced by 4D neural deformation fields in preserving shape
  consistency. This approach is naturally compatible with physics-based
  simulation, ensuring physically plausible interactions. Experiments show that
  AKD achieves superior 3D consistency and motion quality compared with existing
  works on text-to-4D generation. Project page:
  https://research.nvidia.com/labs/dir/akd/
paper_authors._id: 67edf4bf5e87fcaa485a0ad9
paper_authors.name: Xuan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T14:04:06.298Z
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paper_authors.fullname: xuan li
paper_authors.type: user
paper_authors.user: xuanli
paper_authors._id: 67edf4bf5e87fcaa485a0ada
paper_authors.name: Qianli Ma
paper_authors._id: 67edf4bf5e87fcaa485a0adb
paper_authors.name: Tsung-Yi Lin
paper_authors.status: admin_assigned
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paper_authors.fullname: Tsung-Yi Lin
paper_authors.type: user
paper_authors.user: tsungyi
paper_authors._id: 67edf4bf5e87fcaa485a0adc
paper_authors.name: Yongxin Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Yongxin Chen
paper_authors.type: user
paper_authors.user: Ema11
paper_authors._id: 67edf4bf5e87fcaa485a0add
paper_authors.name: Chenfanfu Jiang
paper_authors.status: admin_assigned
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paper_authors._id: 655683727be68c0961673f45
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paper_authors.fullname: Chenfanfu Jiang
paper_authors.type: user
paper_authors.user: cffjiang
paper_authors._id: 67edf4bf5e87fcaa485a0ade
paper_authors.name: Ming-Yu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T14:03:21.297Z
paper_authors._id: 62f049afdf4b93aad5c7f2d6
paper_authors.avatarUrl: /avatars/e272e58ad996733d7098e50248e5b57e.svg
paper_authors.fullname: Ming-Yu Liu
paper_authors.type: user
paper_authors.user: mingyuliutw
paper_authors._id: 67edf4bf5e87fcaa485a0adf
paper_authors.name: Donglai Xiang
paper_title: Articulated Kinematics Distillation from Video Diffusion Models
paper_summary: We present Articulated Kinematics Distillation (AKD), a framework for
  generating high-fidelity character animations by merging the strengths of
  skeleton-based animation and modern generative models. AKD uses a
  skeleton-based representation for rigged 3D assets, drastically reducing the
  Degrees of Freedom (DoFs) by focusing on joint-level control, which allows for
  efficient, consistent motion synthesis. Through Score Distillation Sampling
  (SDS) with pre-trained video diffusion models, AKD distills complex,
  articulated motions while maintaining structural integrity, overcoming
  challenges faced by 4D neural deformation fields in preserving shape
  consistency. This approach is naturally compatible with physics-based
  simulation, ensuring physically plausible interactions. Experiments show that
  AKD achieves superior 3D consistency and motion quality compared with existing
  works on text-to-4D generation. Project page:
  https://research.nvidia.com/labs/dir/akd/
paper_ai_summary: Articulated Kinematics Distillation merges skeletal animation and diffusion models to generate high-fidelity, structurally consistent, and physically plausible character animations.
paper_ai_keywords: skeleton-based animation generative models Degrees of Freedom (DoFs) Score Distillation Sampling (SDS) video diffusion models shape consistency physics-based simulation