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arXiv Computer Vision By Yuming Zhao, Zangyueyang Xian, Qijian Zhang, Rendong Liang, Qin Jia, Ying He, Junhui Hou

SeamFlow: Structure-Aware Flow Matching on Edge Probabilities for Artist-Like UV Unwrapping

Read the original on arXiv Computer Vision →

SeamFlow is a new generative framework for 3D surface cutting and UV unwrapping that reformulates the discrete mesh‑cutting problem as continuous flow matching in a high‑dimensional edge‑probability space. By learning a deterministic mapping from a Gaussian prior to a target seam‑probability distribution and using an evolution network to couple local topological tokens with global shape priors, SeamFlow guides smooth probability flow through ODE solving. Compared with existing autoregressive generative methods, SeamFlow improves topology awareness, eliminates 3D spatial projection errors and artificial sequential‑order bias, and achieves exceptional semantic coherence with remarkably low parameterization distortion.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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