AniGaussian: Animatable Gaussian Avatar With Pose‐Guided Deformation

Abstract Recent advancements in Gaussian‐based human body reconstruction have achieved notable success in creating animatable avatars. However, there are ongoing challenges to fully exploit the SMPL model's prior knowledge and enhance the visual fidelity of these models to achieve more refined avatar reconstructions. In this paper, we introduce AniGaussian, which addresses the above issues with two insights. First, we propose an innovative pose‐guided deformation strategy that effectively constrains the dynamic Gaussian avatar using SMPL pose guidance, ensuring the reconstructed model not only captures detailed surface nuances but also maintains anatomical correctness across a wide range of motions. Second, we address the limitations of Gaussian models in representing dynamic human bodies in terms of expressiveness. We propose a novel canonical‐to‐observation rigidity prior to constrain the deformation field, which ensures geometric stability and significantly reduces unexpected artefacts during novel motions. Furthermore, we introduce a split‐with‐scale strategy that significantly improves geometry quality. The ablative study experiment demonstrates the effectiveness of our innovative model design. Through extensive comparisons with existing methods, AniGaussian demonstrates superior performance in both qualitative results and quantitative metrics.