activity
20212026
most citedTEXGen: a Generative Diffusion Model for Mesh Textures

22 citations · 32 across the 11 of their papers we have counts for

collaborators
Showing cs.CVShow all

10 papers · 1 filter

cs.CV2026

Nexus: Native Mesh Generation with Diffusion

Hanxiao Wang, Ying-Tian Liu, Yuan-Chen Guo +5

Generating high-quality triangle meshes is essential for film, gaming, and interactive 3D applications. Mainstream methods rely on mesh serialization and autoregressive processes,…

cs.CV2026

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

Hao Xu, Yuqing Zhang, Yiqian Wu +5

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 geometr…

cs.CV2026

FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation

Hanxiao Wang, Yuan-Chen Guo, Ying-Tian Liu +6

Autoregressive models for 3D mesh generation suffer from a fundamental limitation: they flatten meshes into long vertex-coordinate sequences. This results in prohibitive computatio…

cs.CV2025★ 1 cited

OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion

Yunhan Yang, Yufan Zhou, Yuan-Chen Guo +7

The creation of 3D assets with explicit, editable part structures is crucial for advancing interactive applications, yet most generative methods produce only monolithic shapes, lim…

cs.CV2024★ 22 cited

TEXGen: a Generative Diffusion Model for Mesh Textures

Xin Yu, Ze Yuan, Yuan-Chen Guo +6

While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets…

cs.CV2024

3D Gaussian Editing with A Single Image

Guan Luo, Tian-Xing Xu, Ying-Tian Liu +3

The modeling and manipulation of 3D scenes captured from the real world are pivotal in various applications, attracting growing research interest. While previous works on editing h…