Research map / 3D vision / Face and shape
Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters
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Make-It-Animatable is a data-driven feed-forward framework that predicts bones, skinning blend weights, and rest-pose transforms for arbitrary-pose 3D humanoid meshes or Gaussian splats in about one second.
Problem
Automatic rigging and skinning of 3D characters requires manual annotation, is limited to rigid skeleton topologies and canonical poses, and template-based avatar generation is restricted to realistic human shapes.
Approach
A particle-based shape autoencoder (3DShape2VecSet-style frozen encoder with geometry-aware normal attention) decodes blend weights and bones via learnable queries; a structure-aware causal transformer predicts bones parent-to-child along the kinematic tree, with body prior losses, and a coarse-to-fine canonicalized, hierarchically sampled training scheme.
Key results
- On the bipedal subset, skeleton IoU 82.50% vs 53.50% for RigNet, and CD-J2J 4.49% vs 6.63%.
- Full pipeline achieves weights error 4.70%, joints error 2.11%, poses error 13.6% on the Mixamo test split, with ablations showing each component helps.
- Inference takes under one second per character, with the paper citing about 0.5 s on an 8k-vertex mesh.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Make-It-Animatable instead of RigNet | skeleton prediction · bipedal humanoid subset of test dataset | IoU: 53.50% -> 82.50% | Helped |
| Use Make-It-Animatable instead of RigNet | skeleton prediction · bipedal humanoid subset of test dataset | CD-J2J: 6.63% -> 4.49% | Helped |
| Add canonical transformation (ablation) | Make-It-Animatable · Mixamo test split | Weights Error: 6.27% -> 4.70% | Helped |
| Add canonical transformation (ablation) | Make-It-Animatable · Mixamo test split | Poses Error: 41.8% -> 13.6% | Helped |
| Add hierarchical sampling (ablation) | Make-It-Animatable · Mixamo test split | Joints Error: 2.42% -> 2.11% | Helped |
| Add hierarchical sampling (ablation) | Make-It-Animatable · Mixamo test split | Poses Error: 18.0% -> 13.6% | Helped |
| Add geometry-aware attention (ablation) | Make-It-Animatable · Mixamo test split | Weights Error: 5.16% -> 4.70% | Helped |
| Add structure-aware transformer (ablation) | Make-It-Animatable · Mixamo test split | Poses Error: 14.9% -> 13.6% | Helped |
| Add body prior loss (ablation) | Make-It-Animatable · Mixamo test split | Poses Error: 14.0% -> 13.6% | Helped |
| Use global dual-quaternion pose representation instead of the ablated pose representation (ablation) | Make-It-Animatable · Mixamo test split | Poses Error: 35.3% -> 13.6% | Helped |
Surprising findings
- Template-based SMPL avatar methods (TADA, HumanGaussian) yield animation quality comparable to or sometimes below the proposed template-free method, though the authors note their outputs stay constrained to realistic human shapes.
Limitations
- Relies on a fixed predefined kinematic tree, mainly a standard human skeleton; extending to non-bipedal characters needs more flexible topology modeling.
- Hierarchical sampling is empirical and hand-designed rather than adaptive.
- Training data is limited to 95 Mixamo characters plus 35 VRoid characters.
Experimental setup
Datasets: Mixamo VRoid Studio characters ModelsResource-RigNetv1 Objaverse
Benchmarks: Mixamo test split ModelsResource-RigNetv1 bipedal subset
Metrics: percentage error of weights, joints, poses IoU precision recall CD-J2J CD-J2B CD-B2B
Baselines: Meshy Tripo RigNet TADA HumanGaussian
Builds on: 3DShape2VecSet RigNet TARig Pinocchio
Closest papers by meaning
- HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset (CVPR 2025 · similarity 0.88)
- MagicArticulate: Make Your 3D Models Articulation-Ready (CVPR 2025 · similarity 0.87)
- DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters (CVPR 2025 · similarity 0.87)
- GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning (CVPR 2024 · similarity 0.87)
- AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction (CVPR 2025 · similarity 0.87)
- 3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting (CVPR 2024 · similarity 0.87)
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