Siavatar: Animatable 3D Gaussian Avatar from a Single Image

Despite the progress of 3D Gaussian Splatting (3DGS), reconstructing one-shot animatable 3D human avatars from a single image remains a challenging task. Existing 3DGS-based methods primarily rely on appearance observation and motion cues from monocular videos to reconstruct animatable 3D avatars. However, when applied to single-image setups, these methods struggle to extract accurate 3D features from a 2D image, limiting their ability to capture fine-grained appearance details and dynamic deformation, especially from challenging perspectives. In this work, we propose SIAvatar, a novel single-image human reconstruction method that integrates diffusion-based appearance prior and parametric human model geometry prior within a 3DGS framework. Secondly, a vertex-based adaptive Gaussian densification scheme is introduced to effectively represent human geometry while mitigating artifacts. Extensive experiments demonstrate that SIAvatar generates realistic 3D avatars with plausible appearance details and novel pose animation from a single input image. Project page: https://siavatar.github.io/.