Zehuan Huang (黄泽桓)Final Year Master Student @ Beihang University
Email: huangzehuan@buaa.edu.cn |
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I am a master student in School of Software from Beihang University now, supervised by Prof. Lu Sheng.
My prior research focused on applying deep generative models to 3D asset creation, encompassing the generation of 3D objects, scenes, textures, and animations. My current research interests lie in world models and simulation, including
(i) Generalizable 3D Foundation Models (Generative 3D
Reconstruction, Physics Modeling)
(ii) Interactive World Models (Real-Time, Long-term
Memory,
Physics-Compliance)
I am grateful to all my collaborators and mentors along the way. I first started doing research under the guidance of Prof. Miao Wang. Then I started working on deep learning related projects under the supervision of Prof. Lu Sheng. Besides, I also successively haved intern at MiniMax, Shanghai AI Lab, and VAST, and I'm fortunate to have worked closely with Junting Dong, Yuan-Chen Guo and Yanpei Cao.
I am always open to academic and industrial collaborations, if you share the vision, please do not hesitate to contact me!
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Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive
Diffusion
CVPR 2025
TL;DR: Transfer the two-stage image-to-3D pipeline
into a unified recursive diffusion process, thereby reducing the data bias of each stage
and improving the quality of generated 3D.
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EpiDiff: Enhancing Multi-View Synthesis via Localized
Epipolar-Constrained Diffusion
CVPR 2024
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Multi-Agent Amodal Completion: Direct Synthesis with
Fine-Grained Semantic Guidance
ACM MM 2025
Comming soon...
TL;DR: A multi-agent framework for high-fidelity
amodal completion.
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Personalize Anything for Free with Diffusion Transformer
Under Review
TL;DR: Customize any subject with advanced DiT without
additional fine-tuning.
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Parts2Whole: Generalizable Multi-Part Portrait
Customization
TIP 2025
TL;DR: A unified framework for customizing human
images
from user-specified part images.
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