Dynamic human relighting is a complex task with significant demand across various applications. Its core challenge lies in effectively handling both dynamic human geometry reconstruction and material estimation. Existing works mainly achieve human reconstruction and relighting with Neural Radiance Fields, but they are not only inefficient, but also still inaccurate in material estimation and relighting. In this paper, we propose a novel approach called ReGA, which leverages efficient 3D Gaussian Splatting to create animatable and relightable avatars from sparse-view human motion. The training process consists of two stages: the geometry stage and the material stage. To overcome the geometric weakness of vanilla Gaussian representation, we introduce dynamic alignment mechanism in the geometry stage, combining the advantages of Gaussian splatting and mesh-based representation to produce reasonable human surface. In the material stage, we enhance the inverse rendering process by introducing twofold correlation strategies that establish chrominance correlation between Gaussian radiance color and albedo. Experiments demonstrate that our method outperforms existing approaches in dynamic human relighting task.

While numerous studies have explored NeRF-based novel view synthesis for dynamic humans, they often require training that exceeds several hours, limiting their practicality. Efforts to improve training efficiency have also encountered challenges because it is hard to optimize non-rigid transformations, thus leading to coarse renderings. In this work, we introduce an innovative approach for efficiently learning and integrating neural human representations. To achieve this, we propose a comprehensive utilization of the features stored in both canonical and observational spaces, facilitated through a collaborative refinement process that integrates canonical representations with observational details. Specifically, we initially propose decomposing high-dimensional multi-space feature volume into several feature planes, subsequently utilizing matrix multiplication to explicitly establish the correlations between different planes. This enables the simultaneous optimization of their counterparts across all dimensions by optimizing interpolated features, efficiently integrating associated details, and accelerating the rate of convergence. Additionally, we use the proposed collaborative refinement process to iteratively enhance the canonical representation. By integrating multi-space representations, we further facilitate the co-optimization of multiple frames' time-dependent observations. Experiments demonstrate that our method can achieve high-quality free-viewpoint renderings within nearly 5 minutes of optimization. Compared to state-of-the-art approaches, our results show more realistic rendering details, marking a significant advancement in both performance and efficiency.