DreamUV introduces an end-to-end learning framework that treats UV unwrapping as a generative Flow Matching problem. It learns a mesh-conditioned transport process to generate artist-like UV layouts, with boundary-aware training and Model-in-the-Loop fine-tuning to ensure seam geometry and practical validity. Results show straighter seams, tighter axis-aligned islands, and superior alignment with professional artist preferences.