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Autores principales: Zhang, Zheng, Zhang, Qinchuan, Ye, Yuteng, Chen, Zhi, Ji, Penglei, Li, Mengfei, Zhang, Wenxiao, Liu, Yuan
Formato: Preprint
Publicado: 2026
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Acceso en línea:https://arxiv.org/abs/2603.15436
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author Zhang, Zheng
Zhang, Qinchuan
Ye, Yuteng
Chen, Zhi
Ji, Penglei
Li, Mengfei
Zhang, Wenxiao
Liu, Yuan
author_facet Zhang, Zheng
Zhang, Qinchuan
Ye, Yuteng
Chen, Zhi
Ji, Penglei
Li, Mengfei
Zhang, Wenxiao
Liu, Yuan
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_15436
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle MV2UV: Generating High-quality UV Texture Maps with Multiview Prompts
Zhang, Zheng
Zhang, Qinchuan
Ye, Yuteng
Chen, Zhi
Ji, Penglei
Li, Mengfei
Zhang, Wenxiao
Liu, Yuan
Computer Vision and Pattern Recognition
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.
title MV2UV: Generating High-quality UV Texture Maps with Multiview Prompts
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2603.15436