DreamUV: Unwrap Artist-like UV by End-to-End Flow Matching

2026-06-21

AI Summary

Traditional UV unwrapping methods struggle to simultaneously minimize geometric distortion and satisfy artists’ stylistic preferences, such as straight seams and axis-aligned UV islands. This work formulates UV unwrapping for the first time as an end-to-end flow-matching generative problem, learning a mesh-conditioned transport process that maps noise to artist-style UV layouts, thereby producing diverse, production-ready results. To bridge the gap between geometric fidelity and artistic style, the authors introduce a boundary-aware loss and a model-in-the-loop fine-tuning mechanism. Evaluated on a large-scale professional dataset, the proposed method significantly outperforms existing approaches, generating notably straighter seams and more compact, axis-aligned UV islands while maintaining low distortion—results that achieve strong approval from professional artists.

Abstract

UV parameterization is a fundamental step in 3D content creation, yet producing production-ready UV layouts remains challenging due to the gap between geometric distortion objectives and the stylistic preferences of professional artists. While classical methods optimize handcrafted energy functions, artist-authored UVs exhibit structural patterns such as straightened seams, axis-aligned islands, and flexible interior deformation, properties that are difficult to explicitly formulate. In this work, we present DreamUV, an end-to-end learning framework that formulates UV unwrapping as a generative Flow Matching problem. Rather than predicting a single optimal parameterization, DreamUV learns a mesh-conditioned transport process that maps noise samples to a distribution of artist-like UV layouts. To reflect real-world authoring practices, we introduce a boundary-aware training strategy that prioritizes seam geometry, and a Model-in-the-Loop Finetuning(MITL) scheme that explicitly accounts for discretization errors during sampling and stabilizes transport dynamics under heterogeneous supervision. We evaluate DreamUV on a large-scale dataset of professionally authored UV layouts. Experiments demonstrate that our method produces significantly straighter boundaries and tighter axis-aligned islands than both classical and learning-based baselines, while maintaining competitive distortion metrics. Qualitative results and a user study with professional artists further confirm that DreamUV generates UV layouts that are not only valid, but aligned with practical production requirements.

Problem

• UV parameterization
• artist-like UV
• 3D content creation
• seam geometry
• axis-aligned islands

Innovation

• Flow Matching
• UV parameterization
• artist-like UV
• boundary-aware training
• Model-in-the-Loop Finetuning