MV2UV: Generating High-quality UV Texture Maps with Multiview Prompts

2026-03-16

AI Summary

This work addresses the challenges of view inconsistency and missing textures in occluded regions inherent in multi-view texture generation, as well as the limited generalization and inability of conventional UV inpainting methods to leverage 2D diffusion priors. To overcome these limitations, we propose a unified framework that integrates multi-view image generation priors with a UV-space generative model. Our approach simultaneously inpaints occluded regions and enforces multi-view consistency directly in UV space, effectively combining the rich semantic priors of 2D diffusion models with the geometric coherence of UV representations. Experimental results demonstrate that our method significantly improves texture quality in both unseen and view-conflicting regions, outperforming existing approaches and achieving, for the first time, effective synergy between multi-view generative priors and UV-space generative modeling.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Generating high-quality textures for 3D assets is a challenging task. Existing multiview texture generation methods suffer from the multiview inconsistency and missing textures on unseen parts, while UV inpainting texture methods do not generalize well due to insufficient UV data and cannot well utilize 2D image diffusion priors. In this paper, we propose a new method called MV2UV that combines 2D generative priors from multiview generation and the inpainting ability of UV refinement to get high-quality texture maps. Our key idea is to adopt a UV space generative model that simultaneously inpaints unseen parts of multiview images while resolving the inconsistency of multiview images. Experiments show that our method enables a better texture generation quality than existing methods, especially in unseen occluded and multiview-inconsistent parts.

Problem

• UV texture generation
• multiview inconsistency
• texture inpainting
• 3D asset texturing
• unseen region completion

Innovation

• MV2UV
• UV texture generation
• multiview consistency
• diffusion priors
• texture inpainting