title: Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready
    3D Characters
summary: 3D characters are essential to modern creative industries, but making them
  animatable often demands extensive manual work in tasks like rigging and
  skinning. Existing automatic rigging tools face several limitations, including
  the necessity for manual annotations, rigid skeleton topologies, and limited
  generalization across diverse shapes and poses. An alternative approach is to
  generate animatable avatars pre-bound to a rigged template mesh. However, this
  method often lacks flexibility and is typically limited to realistic human
  shapes. To address these issues, we present Make-It-Animatable, a novel
  data-driven method to make any 3D humanoid model ready for character animation
  in less than one second, regardless of its shapes and poses. Our unified
  framework generates high-quality blend weights, bones, and pose
  transformations. By incorporating a particle-based shape autoencoder, our
  approach supports various 3D representations, including meshes and 3D Gaussian
  splats. Additionally, we employ a coarse-to-fine representation and a
  structure-aware modeling strategy to ensure both accuracy and robustness, even
  for characters with non-standard skeleton structures. We conducted extensive
  experiments to validate our framework's effectiveness. Compared to existing
  methods, our approach demonstrates significant improvements in both quality and
  speed.
paper_authors._id: 6747ff92d3cce579b6e34efa
paper_authors.name: Zhiyang Guo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-28T09:19:24.694Z
paper_authors._id: 65fba7bd0b78c48c9e39e0f1
paper_authors.avatarUrl: /avatars/ca432ead932cb7c641f9375b9653ad0d.svg
paper_authors.fullname: Zhiyang Guo
paper_authors.type: user
paper_authors.user: jasongzy
paper_authors._id: 6747ff92d3cce579b6e34efb
paper_authors.name: Jinxu Xiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T12:27:46.277Z
paper_authors._id: 6321fdcc6b1992383fa3e405
paper_authors.avatarUrl: /avatars/69a010d43c78f46cb5d0e4a5dd3ef9f1.svg
paper_authors.fullname: Jinxu Xiang
paper_authors.type: user
paper_authors.user: PhantomXJX
paper_authors._id: 6747ff92d3cce579b6e34efc
paper_authors.name: Kai Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T12:27:53.098Z
paper_authors._id: 65f9ba6a1e5b57dc4f831e38
paper_authors.avatarUrl: /avatars/941aa0e217837f4532cbc37a6580c62c.svg
paper_authors.fullname: Kai Ma
paper_authors.type: user
paper_authors.user: KaiMa-endeavour
paper_authors._id: 6747ff92d3cce579b6e34efd
paper_authors.name: Wengang Zhou
paper_authors._id: 6747ff92d3cce579b6e34efe
paper_authors.name: Houqiang Li
paper_authors._id: 6747ff92d3cce579b6e34eff
paper_authors.name: Ran Zhang
paper_title: Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready
    3D Characters
paper_summary: 3D characters are essential to modern creative industries, but making them
  animatable often demands extensive manual work in tasks like rigging and
  skinning. Existing automatic rigging tools face several limitations, including
  the necessity for manual annotations, rigid skeleton topologies, and limited
  generalization across diverse shapes and poses. An alternative approach is to
  generate animatable avatars pre-bound to a rigged template mesh. However, this
  method often lacks flexibility and is typically limited to realistic human
  shapes. To address these issues, we present Make-It-Animatable, a novel
  data-driven method to make any 3D humanoid model ready for character animation
  in less than one second, regardless of its shapes and poses. Our unified
  framework generates high-quality blend weights, bones, and pose
  transformations. By incorporating a particle-based shape autoencoder, our
  approach supports various 3D representations, including meshes and 3D Gaussian
  splats. Additionally, we employ a coarse-to-fine representation and a
  structure-aware modeling strategy to ensure both accuracy and robustness, even
  for characters with non-standard skeleton structures. We conducted extensive
  experiments to validate our framework's effectiveness. Compared to existing
  methods, our approach demonstrates significant improvements in both quality and
  speed.
paper_ai_keywords: particle-based shape autoencoder blend weights bones pose transformations coarse-to-fine representation structure-aware modeling strategy Gaussian splats