Intrinsic Geometry-Appearance Consistency Optimization for Sparse-View Gaussian Splatting

• URL: http://arxiv.org/abs/2603.02893v1
• Date: Tue, 03 Mar 2026 11:44:46 GMT
• Title: Intrinsic Geometry-Appearance Consistency Optimization for Sparse-View Gaussian Splatting
• Authors: Kaiqiang Xiong, Rui Peng, Jiahao Wu, Zhanke Wang, Jie Liang, Xiaoyun Zheng, Feng Gao, Ronggang Wang,
• Abstract summary: 3D human reconstruction from a single image is a challenging problem.<n>We present emphMVD-HuGaS, enabling free-view 3D human rendering from a single image via a multi-view human diffusion model.
• Score: 36.3168821104293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D human reconstruction from a single image is a challenging problem and has been exclusively studied in the literature. Recently, some methods have resorted to diffusion models for guidance, optimizing a 3D representation via Score Distillation Sampling(SDS) or generating a back-view image for facilitating reconstruction. However, these methods tend to produce unsatisfactory artifacts (\textit{e.g.} flattened human structure or over-smoothing results caused by inconsistent priors from multiple views) and struggle with real-world generalization in the wild. In this work, we present \emph{MVD-HuGaS}, enabling free-view 3D human rendering from a single image via a multi-view human diffusion model. We first generate multi-view images from the single reference image with an enhanced multi-view diffusion model, which is well fine-tuned on high-quality 3D human datasets to incorporate 3D geometry priors and human structure priors. To infer accurate camera poses from the sparse generated multi-view images for reconstruction, an alignment module is introduced to facilitate joint optimization of 3D Gaussians and camera poses. Furthermore, we propose a depth-based Facial Distortion Mitigation module to refine the generated facial regions, thereby improving the overall fidelity of the reconstruction. Finally, leveraging the refined multi-view images, along with their accurate camera poses, MVD-HuGaS optimizes the 3D Gaussians of the target human for high-fidelity free-view renderings. Extensive experiments on Thuman2.0 and 2K2K datasets show that the proposed MVD-HuGaS achieves state-of-the-art performance on single-view 3D human rendering.

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