UniCross3D improves single-image 3D models with better view and color matching
UniCross3D: Unified Cross-View and Cross-Domain Diffusion for Consistent Single-Image 3D Generation
Computer Vision and Pattern RecognitionGraphics
Summary
Creating detailed 3D models from just one photo is hard because some parts are hidden, lighting changes, and colors can be tricky to interpret. The authors introduce UniCross3D, a new method that helps 3D models look consistent from different angles and match color and shape better. They use special techniques to keep the models realistic when viewed from any viewpoint and make the colors and shapes line up properly. Their tests show improvements especially on tricky images with confusing textures or viewpoints.
What this means in practice
- •For game developers: Generate detailed and consistent 3D objects from single photos to speed up asset creation for games.
- •For virtual reality content creators: Produce realistic 3D models from limited input images to enhance immersive VR environments with fewer resources.
Authors
U-Chae Jun, Jaeeun Ko, Jiwoo Kang
Abstract
Reconstructing detailed geometry and realistic appearance from a single RGB image is essential yet fundamentally challenging due to inherent ambiguities such as occlusion, lighting variations, and texture-geometry entanglement. While recent diffusion-based generative models have significantly improved novel view synthesis, existing approaches suffer from two critical limitations: lack of cross-view geometric consistency and insufficient cross-domain semantic alignment. To address these issues, we introduce UniCross3D, a unified cross-view and cross-domain diffusion framework designed explicitly for consistent and physically coherent 3D generation. UniCross3D features two novel contributions: (1) a cross-view latent regularization that enforces cross-view geometric consistency across synthesized viewpoints by penalizing latent variance, and (2) a cross-domain mutual information objective grounded in the physics of image formation, explicitly aligning synthesized color and normal maps. Extensive experiments demonstrate that UniCross3D achieves significantly improved view consistency and semantic alignment over state-of-the-art methods and yields higher-fidelity reconstructions, particularly under challenging textures and ambiguous viewpoints.