GaussianHand: Real-Time 3D Gaussian Rendering for Hand Avatar Animation

Rendering animatable and realistic hand avatars is pivotal for enhancing user experiences in human-centered AR/VR applications. While recent initiatives have utilized neural radiance fields to forge hand avatars with lifelike appearances, these methods are often hindered by high computational demands and the necessity for extensive training views. In this paper, we introduce GaussianHand, the first Gaussian-based real-time 3D rendering approach that enables efficient free-view and free-pose hand avatar animation from sparse view images. Our approach encompasses two key innovations. We first propose Hand Gaussian Blend Shapes that effectively models hand surface geometry while ensuring consistent appearance across various poses. Secondly, we introduce the Neural Residual Skeleton, equipped with Residual Skinning Weights, designed to rectify inaccuracies involved in Linear Blend Skinning deformations due to geometry offsets. Experiments demonstrate that our method not only achieves far more realistic rendering quality with as few as 5 or 20 training views, compared to the 139 views required by existing methods, but also excels in efficiency, achieving up to 125 frames per second for real-time rendering and remarkably surpassing recent methods.