AniGaussian: Animatable Gaussian Avatar with Pose-guided Deformation
This work addresses the problem of enhancing visual fidelity and anatomical accuracy in Gaussian-based human avatar reconstruction for applications in animation and virtual reality, representing an incremental improvement over prior methods.
The paper tackles the challenge of creating high-fidelity animatable human avatars by introducing AniGaussian, which uses a pose-guided deformation strategy and rigid-based priors to improve anatomical correctness and geometry quality, achieving superior performance in qualitative and quantitative metrics compared to existing methods.
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.