SinGS: Animatable Single-Image Human Gaussian Splats with Kinematic Priors

Despite significant advances in accurately estimating geometry in contemporary single-image 3D human reconstruction, creating a high-quality, efficient, and animatable 3D avatar remains an open challenge. Two key obstacles persist: incomplete observation and inconsistent 3D priors.To address these challenges, we propose \textbf{SinG}, aiming to achieve high-quality and efficient animatable 3D avatar reconstruction.At the heart of SinG are two key components: Kinematic Human Diffusion (KHD) and compact 3D distillation. The former is a foundational human model that samples within pose space to generate a highly 3D-consistent and high-quality sequence of human images, inferring unseen viewpoints and providing kinematic priors. The latter is a system that reconstructs a compact, high-quality 3D avatar even under imperfect priors, achieved through a novel regional Laplacian that enables precise and compact assembly of 3D primitives.Extensive experiments demonstrate that SinG enables lifelike, animatable human reconstructions, maintaining both high quality and inference efficiency.