Research Interests: My research focuses on
3D AIGC & World Models. I aspire to build high-quality, practical, and efficient 3D digital worlds towards the AGI era.
Our work SUPRA-Tex is now open source! It generates relightable 3D assets from a single image while preserving fine lettering, patterns, and material details. Paper / Code.
[*] equal contribution; [†] corresponding author(s);
Representative papers are highlighted.
SUPRA-Tex: 3D Generation with Super-Aligned Texture
Chen Yang*, Jiemin Fang*†, Fanpeng Meng*, Zihao Huang, Jichen Pang, Guanjun Wu, Zhikuan Bao, Qi Tian† arXiv, 2026 Webpage /
Paper /
Code
Generating relightable 3D assets with reference-aligned textures that preserve fine lettering, patterns, and material details.
Gate3DGen: A Pre-processing Model that Selectively Purifies Real-World Images for 3D Generation
Xiaolin Li*, Zihao Huang*, Fanpeng Meng, Chen Yang, Guanjun Wu, Kai Zhang†, Jiemin Fang†, Qi Tian† arXiv, 2026 Webpage /
Paper /
Code
Selectively purifying degraded real-world images for existing 3D generators while preserving object identity and visual details. Providing higher-quality inputs for 3D generation.
Generating videos with controllable dynamics from coarse proxies and text prompts, preserving motion and interactions without additional training.
DreamDissector: Learning Disentangled Text-to-3D Generation from 2D Diffusion Priors
Zizheng Yan, Jiapeng Zhou, Fanpeng Meng, Yushuang Wu, Lingteng Qiu, Zisheng Ye, Shuguang Cui, Guanying Chen, Xiaoguang Han† ECCV, 2024 Paper
Disentangling text-generated 3D scenes into independent textured meshes.
3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification
Jiazhao Zhang*, Liu Dai*, Fanpeng Meng, Qingnan Fan, Xuelin Chen, Kai Xu, He Wang† CVPR, 2023 Webpage /
Paper /
Code
Proposing a framework for the challenging 3D-aware ObjectNav with two straight-forward sub-policies,
namely corner-guided exploration policy and category-aware identification policy.