Learning Animation-Aware 3D Gaussians with Native Rigging

3D Gaussian Splatting (3DGS) has achieved remarkable performance in novel-view synthesis, yet directly converting static 3DGS into animatable assets remains challenging. Existing automatic rigging methods primarily target mesh-based representations, while large-scale rigged 3D Gaussian datasets are lacking. Moreover, even after rigging, Gaussians optimized for static rendering can produce artifacts during animation, particularly around joints and in poorly observed regions. To address these challenges, we present a framework that integrates automatic rigging with animation-aware Gaussian generation. Our method predicts skeletal structure and skinning weights directly from static 3DGS, then uses the predicted skeleton to guide Gaussian adaptation. This process refines Gaussian distributions and shapes to improve deformation robustness while preserving the original appearance. To support learning, we introduce a data construction pipeline that produces a large-scale paired dataset of vanilla and animation-aware Gaussian representations, together with skeletons and skinning weights. Extensive experiments demonstrate the effectiveness of our framework in automatic rigging and reducing deformation-induced rendering artifacts.