Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready
  3D Characters
Authors: name: Zhiyang Guo, name: Jinxu Xiang, name: Kai Ma, name: Wengang Zhou, name: Houqiang Li, name: Ran Zhang

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.