Rig, Render, Refine: Agentic 3D Rigging through Iterative Feedback

We present an agent-based approach to 3D mesh rigging and animation that frames rigging as an iterative cycle of perception, construction, and verification. Existing methods typically predict skeletons and skinning weights directly from 3D geometry, but geometry alone may not determine how an object should articulate or whether a proposed rig will produce plausible deformations under motion. Our key idea is to inspect a rig’s behavior under articulation and use these observations to guide its refinement. Starting from a static mesh, we construct and iteratively refine a 3D skeleton by integrating multiview renderings, geometric structure, mesh-part information, and 2D skeleton proposals. We then generate skinning weights and evaluate the resulting rig under diagnostic poses, using observed deformation errors to refine both the weights and the skeleton. Experiments on a challenging dataset of structurally diverse 3D assets demonstrate improved skeleton and deformation accuracy over state-of-the-art rigging methods.