Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready
  3D Characters
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.
6747ff92d3cce579b6e34efa
Zhiyang Guo
claimed_verified
2024-11-28T09:19:24.694Z
65fba7bd0b78c48c9e39e0f1
/avatars/ca432ead932cb7c641f9375b9653ad0d.svg
Zhiyang Guo
user
jasongzy
6747ff92d3cce579b6e34efb
Jinxu Xiang
admin_assigned
2024-11-28T12:27:46.277Z
6321fdcc6b1992383fa3e405
/avatars/69a010d43c78f46cb5d0e4a5dd3ef9f1.svg
Jinxu Xiang
user
PhantomXJX
6747ff92d3cce579b6e34efc
Kai Ma
admin_assigned
2024-11-28T12:27:53.098Z
65f9ba6a1e5b57dc4f831e38
/avatars/941aa0e217837f4532cbc37a6580c62c.svg
Kai Ma
user
KaiMa-endeavour
6747ff92d3cce579b6e34efd
Wengang Zhou
6747ff92d3cce579b6e34efe
Houqiang Li
6747ff92d3cce579b6e34eff
Ran Zhang
Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready
  3D Characters
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.
Make-It-Animatable is a data-driven method that converts 3D humanoid models into animatable avatars quickly and effectively using a particle-based shape autoencoder and a coarse-to-fine strategy.
particle-based shape autoencoder coarse-to-fine representation structure-aware modeling strategy