AniGaussian: Animatable Gaussian Avatar with Pose-guided Deformation

TL;DR

AniGaussian introduces a pose-guided deformation method and enhanced priors to improve the visual fidelity and dynamic expressiveness of Gaussian-based animatable human avatars, achieving superior results over existing approaches.

Contribution

The paper presents a novel pose-guided deformation strategy and a split-with-scale approach to enhance Gaussian avatar reconstruction, addressing prior limitations in expressiveness and detail.

Findings

Achieves higher visual fidelity in avatar reconstruction.

Demonstrates superior quantitative performance compared to existing methods.

Effectively maintains anatomical correctness across motions.

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 with SMPL pose guidance, ensuring that the reconstructed model not only captures the detailed surface nuances but also maintains anatomical correctness across a wide range of motions. Second, we tackle the expressiveness limitations of Gaussian models in representing dynamic human bodies. We incorporate rigid-based priors from previous works to enhance the dynamic transform capabilities of the Gaussian model. 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 result and quantitative metrics.