Auto-Regressive Surface Cutting

YANG LI, Victor Cheung, Xinhai Liu, Yuguang Chen, Zhongjin Luo, Biwen Lei, Haohan Weng, Zibo Zhao, Jingwei Huang, Zhuo Chen, Chunchao Guo

Keywords: surface cutting, Mesh UV unfolding, 3D part segmentation, auto-regressive generative model

TL;DR: An auto-regressive approach that generate seams for surface cutting, with applications in uv parametrization and 3d part decomposition.

Abstract: Surface cutting is a fundamental task in computer graphics, with applications in UV parameterization, texture mapping, and mesh decomposition. However, existing methods often produce technically valid but overly fragmented atlases that lack semantic coherence. We introduce SeamGPT, an auto-regressive model that generates cutting seams by mimicking professional workflows. Our key technical innovation lies in formulating surface cutting as a next token prediction task: sample point clouds on mesh vertices and edges, encode them as shape conditions, and employ a GPT-style transformer to sequentially predict seam segments with quantized 3D coordinates. Our approach achieves exceptional performance on UV unwrapping benchmarks containing both manifold and non-manifold meshes, including artist-created, and 3D-scanned models. In addition, it enhances existing 3D segmentation tools by providing clean boundaries for part decomposition.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 12546