Rig-Reconstruct-Render (R33D): Collaborative Representation for Editable and Skeleton-Drivable 3D Asset Generation

Current 3D generation methods struggle to balance quality, efficiency, and controllability. This work introduces a collaborative 3D representation framework that leverages a proxy mesh as an intermediate representation. On the one hand, the proxy mesh establishes structural associations with the skeleton, guiding the generation of skeleton bindings that better align with target shape characteristics. On the other hand, it enables adaptive Gaussian sampling in the shape space for efficient rendering. Through the multi-level dependencies and collaboration among skeleton, proxy mesh, and 2DGS, image gradients obtained from diffusion models via the SDS method can synchronously and differentiably update the parameters of Gaussians, mesh shapes, and skeletons. This enables efficient generation of high-quality, editable, and driveable assets (with skeleton binding) under user-specified instructions. The proposed framework demonstrates its efficiency, high fidelity, and precise binding results in 3D rigged asset generation tasks.