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.
From a single image, generating relightable PBR 3D assets with reference-aligned textures that preserve fine lettering, patterns, and material details. Trained only on open-source data, it is preferred over five commercial systems, Hunyuan3D 3.1, Rodin 2.5, Tripo H3.1, Meshy 7.1, and Hi3D 3.0, for both visual quality and input alignment across 512 paired human evaluations.
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 Zhangβ , Jiemin Fangβ , Qi Tianβ arXiv, 2026 Webpage /
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Reducing blur, occlusion, and other image degradations while preserving object identity and 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 Hanβ ECCV, 2024 Paper
Disentangling text-generated 3D scenes into independent textured meshes for controllable geometry and texture editing.
3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification
Jiazhao Zhang*, Liu Dai*, Fanpeng Meng, Qingnan Fan, Xuelin Chen, Kai Xu, He Wangβ CVPR, 2023 Webpage /
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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.