ArtUV: Artist-style UV Unwrapping
- URL: http://arxiv.org/abs/2509.20710v1
- Date: Thu, 25 Sep 2025 03:21:21 GMT
- Title: ArtUV: Artist-style UV Unwrapping
- Abstract summary: We introduce ArtUV, a fully automated, end-to-end method for generating artist-style UV unwrapping.<n>We simulate the professional UV mapping process by dividing it into two stages: surface seam prediction and artist-style UV parameterization.<n>Our method ensures semantic consistency and preserves topological structure, making the UV map ready for 2D editing.
- Score: 15.072561481603037
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: UV unwrapping is an essential task in computer graphics, enabling various visual editing operations in rendering pipelines. However, existing UV unwrapping methods struggle with time-consuming, fragmentation, lack of semanticity, and irregular UV islands, limiting their practical use. An artist-style UV map must not only satisfy fundamental criteria, such as overlap-free mapping and minimal distortion, but also uphold higher-level standards, including clean boundaries, efficient space utilization, and semantic coherence. We introduce ArtUV, a fully automated, end-to-end method for generating artist-style UV unwrapping. We simulates the professional UV mapping process by dividing it into two stages: surface seam prediction and artist-style UV parameterization. In the seam prediction stage, SeamGPT is used to generate semantically meaningful cutting seams. Then, in the parameterization stage, a rough UV obtained from an optimization-based method, along with the mesh, is fed into an Auto-Encoder, which refines it into an artist-style UV map. Our method ensures semantic consistency and preserves topological structure, making the UV map ready for 2D editing. We evaluate ArtUV across multiple benchmarks and show that it serves as a versatile solution, functioning seamlessly as either a plug-in for professional rendering tools or as a standalone system for rapid, high-quality UV generation.
Related papers
- DreamUV: Unwrap Artist-like UV by End-to-End Flow Matching [16.305062860577557]
We present DreamUV, an end-to-end learning framework that formulates UV unwrapping as a generative Flow Matching problem.<n>We evaluate DreamUV on a large-scale dataset of professionally authored UV layouts.
arXiv Detail & Related papers (2026-06-21T11:41:32Z) - SeqTex: Generate Mesh Textures in Video Sequence [62.766839821764144]
We introduce SeqTex, a novel end-to-end framework for training 3D texture generative models.<n>We show that SeqTex achieves state-of-the-art performance on both image-conditioned and text-conditioned 3D texture generation tasks.
arXiv Detail & Related papers (2025-07-06T07:58:36Z) - TEXGen: a Generative Diffusion Model for Mesh Textures [63.43159148394021]
We focus on the fundamental problem of learning in the UV texture space itself.
We propose a scalable network architecture that interleaves convolutions on UV maps with attention layers on point clouds.
We train a 700 million parameter diffusion model that can generate UV texture maps guided by text prompts and single-view images.
arXiv Detail & Related papers (2024-11-22T05:22:11Z) - UVMap-ID: A Controllable and Personalized UV Map Generative Model [67.71022515856653]
We introduce UVMap-ID, a controllable and personalized UV Map generative model.
Unlike traditional large-scale training methods in 2D, we propose to fine-tune a pre-trained text-to-image diffusion model.
Both quantitative and qualitative analyses demonstrate the effectiveness of our method in controllable and personalized UV Map generation.
arXiv Detail & Related papers (2024-04-22T20:30:45Z) - Nuvo: Neural UV Mapping for Unruly 3D Representations [61.87715912587394]
Existing UV mapping algorithms operate on geometry produced by state-of-the-art 3D reconstruction and generation techniques.
We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques.
arXiv Detail & Related papers (2023-12-11T18:58:38Z) - Texture Generation on 3D Meshes with Point-UV Diffusion [86.69672057856243]
We present Point-UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with UV mapping to generate high-quality texture images in UV space.
Our method can process meshes of any genus, generating diversified, geometry-compatible, and high-fidelity textures.
arXiv Detail & Related papers (2023-08-21T06:20:54Z) - FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction [46.3392612457273]
This dataset contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions.
Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches.
Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches.
arXiv Detail & Related papers (2022-11-25T03:21:05Z) - TemporalUV: Capturing Loose Clothing with Temporally Coherent UV
Coordinates [37.58323494348772]
We propose a novel approach to generate temporally coherent UV coordinates for loose clothing.
Our method is not constrained by human body outlines and can capture loose garments and hair.
arXiv Detail & Related papers (2022-04-07T18:00:32Z) - AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis [78.17671694498185]
We propose AUV-Net which learns to embed 3D surfaces into a 2D aligned UV space.
As a result, textures are aligned across objects, and can thus be easily synthesized by generative models of images.
The learned UV mapping and aligned texture representations enable a variety of applications including texture transfer, texture synthesis, and textured single view 3D reconstruction.
arXiv Detail & Related papers (2022-04-06T21:39:24Z) - StyleUV: Diverse and High-fidelity UV Map Generative Model [24.982824840625216]
We present a novel UV map generative model that learns to generate diverse and realistic synthetic UV maps without requiring high-quality UV maps for training.
Both quantitative and qualitative evaluations demonstrate that our proposed texture model produces more diverse and higher fidelity textures compared to existing methods.
arXiv Detail & Related papers (2020-11-25T17:19:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.