Title: A$^3$-GS: Animate Any Articulated Objects with 3D Gaussian Splatting
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: generation, 3D, animation, skinning

Abstract: 3D Gaussian representations have demonstrated remarkable success in reconstructing complex scenes and rendering novel views. 
However, rigging and animating template-free articulated objects represented by 3D Gaussians remain challenging. A key difficulty arises from the discretization of Gaussians, which often produces artifacts during animation. Another challenge lies in rigging arbitrary, template-free shapes without sufficient training data. To address these challenges, we propose A$^3$-GS, a new 3D Gaussian splatting-based framework that reconstructs articulated objects from multi-view images while simultaneously estimating a skeleton and skinning weights, thereby enabling rich animations with high-quality rendering. Technically, we first introduce a Mesh–Gaussian hybrid representation for articulated objects. By exploiting the continuity of the mesh, our method mitigates rendering artifacts such as spikes and tearing caused by Gaussian deformation, thereby enhancing the visual quality of animated results. We further learn motion-coherent skinning weights by leveraging motion priors from visual foundation models trained on large-scale 2D video data, reducing reliance on scarce 3D datasets. In addition, we incorporate local rigidity regularization to improve the smoothness of skeleton-based deformation and further suppress artifacts. Extensive experiments validate the effectiveness of our approach, demonstrating clear advantages over existing methods. The code will be made publicly available.