AniGaussian: Animatable Gaussian Avatar with Pose-guided Deformation

• URL: http://arxiv.org/abs/2502.19441v1
• Date: Mon, 24 Feb 2025 06:53:37 GMT
• Title: AniGaussian: Animatable Gaussian Avatar with Pose-guided Deformation
• Authors: Mengtian Li, Shengxiang Yao, Chen Kai, Zhifeng Xie, Keyu Chen, Yu-Gang Jiang,
• Abstract summary: We introduce an innovative pose guided deformation strategy that constrains the dynamic Gaussian avatar with SMPL pose guidance.<n>We incorporate rigid-based priors from previous works to enhance the dynamic transform capabilities of the Gaussian model.<n>Through extensive comparisons with existing methods, AniGaussian demonstrates superior performance in both qualitative result and quantitative metrics.
• Score: 51.61117351997808
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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