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SeamGen model automates UV seam generation for 3D content creation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SeamGen, a novel generative model designed to automate the placement of UV seams in 3D content creation. Unlike previous methods that relied on per-object optimization or semantic proxies, SeamGen learns directly from artist-authored seam layouts using a flow-matching generative model. The system employs a Mesh Transformer backbone, which combines graph attention and self-attention mechanisms to effectively process mesh topology and geometric features. This approach allows SeamGen to generate UV layouts that better align with artist preferences and production requirements, offering improved perceptual quality. AI

IMPACT This model could streamline the 3D content creation pipeline by automating a complex and time-consuming task for artists.

RANK_REASON The cluster contains a research paper detailing a new generative model for a specific task in 3D content creation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SeamGen model automates UV seam generation for 3D content creation

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The cluster contains a research paper detailing a new generative model for a specific task in 3D content creation.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Hao Xu, Yuqing Zhang, Yiqian Wu, Xueqi Ma, Ding Liang, Yan-Pei Cao, Ying-Tian Liu, Xiaogang Jin ·

    SeamGen: Artist-Aligned UV Seam Generation via Graph Flow Matching

    arXiv:2607.12379v1 Announce Type: new Abstract: UV seam placement is a critical yet labor-intensive step in 3D content creation, requiring artists to balance chart shape, seam concealment, and alignment with semantic and geometric features. Existing automatic methods are primaril…

  2. arXiv cs.CV TIER_1 English(EN) · Xiaogang Jin ·

    SeamGen: Artist-Aligned UV Seam Generation via Graph Flow Matching

    UV seam placement is a critical yet labor-intensive step in 3D content creation, requiring artists to balance chart shape, seam concealment, and alignment with semantic and geometric features. Existing automatic methods are primarily based on per-object optimization, relying on h…

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