ArtUV: Artist-style UV Unwrapping

2025-09-24

AI Summary

Existing UV unwrapping methods suffer from high computational cost, fragmented UV islands, semantic inconsistency, and irregular boundaries—hindering artist-driven texture editing. This paper introduces the first end-to-end generative framework for producing high-quality, artist-style UV maps in two stages: (1) a semantic-aware SeamGPT model predicts topologically sound, semantically coherent, and structurally clean seams; (2) an optimization-initialized, autoencoder-guided parameterization jointly minimizes overlap, distortion, and semantic discontinuity while maximizing UV space utilization. Experiments demonstrate significant improvements in topological fidelity and editability across multiple benchmarks. The resulting UV maps are directly compatible with professional rendering pipelines and support rapid standalone generation. Our approach establishes a new paradigm for semantics-controllable, automatic UV unfolding.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Automating artist-style UV unwrapping with semantic seam prediction
• Overcoming limitations of time-consuming and fragmented UV mapping methods
• Generating clean, space-efficient UV maps suitable for 2D editing

Innovation

• Two-stage automated UV unwrapping process
• SeamGPT generates semantic cutting seams
• Auto-Encoder refines optimization-based UV maps