Dense-RAM: Edge-Level Credit Assignment for Reinforcement Learning in Flow-Based UV Unwrapping

UV unwrapping is a fundamental step in creating textured 3D assets. Recent generative methods learn artist-like seam layouts from data, and reinforcement learning can further refine these layouts using UV-quality feedback. However, scalar feedback assigns a shared reward coefficient to all edges in a sampled layout, overlooking differences in local UV quality. To address this limitation, we introduce Dense-RAM, a reinforcement learning framework for post-training flow-matching seam generators. Built on Reinforce Adjoint Matching (RAM), Dense-RAM computes a candidate-level advantage from chart- and layout-level UV feedback, then distributes it across edges according to local quality. The resulting coefficients adapt the reward correction to each edge while preserving the candidate’s mean advantage. Experiments show that Dense-RAM improves chart topology and reduces UV fragmentation. Across RAM, DiffusionNFT, and AWM, edge-level allocation consistently improves final UV quality and reaches comparable quality in fewer optimization steps than scalar feedback.