AniRig: Recovering Animation-Ready Rigged Assets from Arbitrary-Topology 3D Characters

High-quality character meshes are increasingly produced by generation, reconstruction, scanning, and artist workflows. Skeletal animation requires a rest mesh, a skeleton, and skinning weights that work together. Recovering this rigged asset from a single observed mesh remains a bottleneck, especially when the input is already posed. Existing rigging and skinning methods often assume rest-pose inputs or evaluate skeletons and weights separately. Component-level accuracy alone does not establish whether these predictions support coherent rest recovery and subsequent animation. We introduce AniRig, a semantic-hierarchy-conditioned framework for recovering an animation-ready rigged asset from a single observed character mesh with arbitrary topology. The hierarchy specifies joint names and parent-child connectivity, and can be user-provided or inferred from rendered mesh views by a Vision-Language Model. AniRig jointly predicts observed and rest skeletons in a single diffusion model under this hierarchy, and estimates structured sparse skinning weights by jointly evaluating a compact set of candidate joints. A differentiable pose-to-rest construction composes these predictions with the observed mesh to recover the canonical rest mesh and enables coupled fine-tuning with asset-level surface supervision. On Articulation-XL2.0, AniRig improves skeleton and skinning weight accuracy. On an independent real-motion posed-mesh benchmark, asset-level fine-tuning improves canonical rest recovery and held-out re-posing consistency. Qualitative results show that the recovered assets support skeletal animation reuse across matched hierarchies.