RigMo: Unifying Rig and Motion Learning for Generative Animation
Despite significant progress in 4D generation, rig and motion, the core structural and dynamic components of animation are typically modeled as separate problems. Existing pipelines rely on ground-truth skeletons and skinning weights for motion generation and treat auto-rigging as an independent process, undermining scalability and interpretability. We present RigMo, a unified generative framework that jointly learns rig and motion directly from raw mesh sequences, without any human-provided rig annotations. RigMo encodes per-vertex deformations into two compact latent spaces: a rig latent that decodes into explicit Gaussian bones and skinning weights, and a motion latent that produces time-varying SE(3) transformations. Together, these outputs define an animatable mesh with explicit structure and coherent motion, enabling feed-forward rig and motion inference for deformable objects. Beyond unified rig-motion discovery, we introduce a Motion-DiT model operating in RigMo's latent space and demonstrate that these structure-aware latents can naturally support downstream motion generation tasks. Experiments on DeformingThings4D, Objaverse-XL, and TrueBones demonstrate that RigMo learns smooth, interpretable, and physically plausible rigs, while achieving superior reconstruction and category-level generalization compared to existing auto-rigging and deformation baselines. RigMo establishes a new paradigm for unified, structure-aware, and scalable dynamic 3D modeling.
6969e919844a787c4fdea3dd
Hao Zhang
claimed_verified
2026-01-19T09:23:42.347Z
65712f530ea91e592a1e8126
/avatars/2ed00ca40b7ff2b88e4a75ef04807447.svg
Hao Zhang
haoz19
user
haoz19
6969e919844a787c4fdea3de
Jiahao Luo
6969e919844a787c4fdea3df
Bohui Wan
6969e919844a787c4fdea3e0
Yizhou Zhao
6969e919844a787c4fdea3e1
Zongrui Li
6969e919844a787c4fdea3e2
Michael Vasilkovsky
6969e919844a787c4fdea3e3
Chaoyang Wang
6969e919844a787c4fdea3e4
Jian Wang
6969e919844a787c4fdea3e5
Narendra Ahuja
6969e919844a787c4fdea3e6
Bing Zhou
RigMo: Unifying Rig and Motion Learning for Generative Animation
Despite significant progress in 4D generation, rig and motion, the core structural and dynamic components of animation are typically modeled as separate problems. Existing pipelines rely on ground-truth skeletons and skinning weights for motion generation and treat auto-rigging as an independent process, undermining scalability and interpretability. We present RigMo, a unified generative framework that jointly learns rig and motion directly from raw mesh sequences, without any human-provided rig annotations. RigMo encodes per-vertex deformations into two compact latent spaces: a rig latent that decodes into explicit Gaussian bones and skinning weights, and a motion latent that produces time-varying SE(3) transformations. Together, these outputs define an animatable mesh with explicit structure and coherent motion, enabling feed-forward rig and motion inference for deformable objects. Beyond unified rig-motion discovery, we introduce a Motion-DiT model operating in RigMo's latent space and demonstrate that these structure-aware latents can naturally support downstream motion generation tasks. Experiments on DeformingThings4D, Objaverse-XL, and TrueBones demonstrate that RigMo learns smooth, interpretable, and physically plausible rigs, while achieving superior reconstruction and category-level generalization compared to existing auto-rigging and deformation baselines. RigMo establishes a new paradigm for unified, structure-aware, and scalable dynamic 3D modeling.
RigMo is a unified generative framework that simultaneously learns rig and motion from mesh sequences, encoding deformations into compact latent spaces for interpretable and physically plausible 3D animation.
generative framework rig latent motion latent SE(3) transformations auto-rigging deformation baselines latent space mesh sequences animatable mesh structure-aware latents