Qingxiang Lin — accepted AI-conference papers

2026

LATTICE: Democratize High-Fidelity 3D Generation at Scale

Zeqiang Lai, Yunfei Zhao, Zibo Zhao, Haolin Liu, Qingxiang Lin, Jingwei Huang +2

CVPR 2026

We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis benefits from fixed spatial grids and well-established transformer architectures, 3D generation remains fundamentally m

Cited by 0 Source ↗code ↗Scholar ↗

2025

Unleashing Vecset Diffusion Model for Fast Shape Generation

Zeqiang Lai, Yunfei Zhao, Zibo Zhao, Haolin Liu, Fuyun Wang, Huiwen Shi +7

ICCV 2025poster

3D shape generation has greatly flourished through the development of so-called "native" 3D diffusion, particularly through the Vectset Diffusion Model (VDM). While recent advancements have shown promising results in generating high-resolution 3D shapes, VDM still struggles at high-speed generation.…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗