DiGS-Avatar: Single-Image Animatable 3D Human Reconstruction via UV-Space Diffusion

• URL: http://arxiv.org/abs/2608.20759v1
• Date: Fri, 21 Aug 2026 05:46:52 GMT
• Title: DiGS-Avatar: Single-Image Animatable 3D Human Reconstruction via UV-Space Diffusion
• Abstract summary: Single-image 3D human reconstruction often suffers from over-smoothed textures and geometric inconsistencies.<n>We propose DiGS-Avatar, which reformulates this task as an efficient, diffusion-based UV-latent completion task.<n>Experiments demonstrate that DiGS-Avatar achieves state-of-the-art or highly competitive visual fidelity and zero-shot generalization.
• Score: 20.18668110364929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-image 3D human reconstruction often suffers from over-smoothed textures and geometric inconsistencies. While diffusion models improve generative quality, their reliance on multi-view synthesis prior to 3D reconstruction is computationally expensive and prone to view inconsistency. We propose DiGS-Avatar, which reformulates this task as an efficient, diffusion-based UV-latent completion task, ensuring 3D consistency by design. To capture accurate spatial structure, we introduce a teacher-student framework where a multi-view teacher provides geometrically aligned pseudo-ground-truth latents to supervise a single-view diffusion student. Treating this inferred latent as a robust structural skeleton, our method injects high-level semantic features to accurately recover fine textural details without disrupting spatial integrity. The refined representation is then decoded into 3D Gaussian primitives. Extensive experiments demonstrate that DiGS-Avatar achieves state-of-the-art or highly competitive visual fidelity and zero-shot generalization, while reconstructing a fully animatable 3D avatar in just 0.71 seconds. Code is available at https://github.com/KLMAV-CUC/DiGS-Avatar.

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