Haodong Li

I'm a second-year M.Phil. student at HKUST (Guangzhou), working with Prof. Ying-Cong Chen. I'm currently doing an internship at Tencent Hunyuan (3D Generation Group). I got my B.Eng. degree from Zhejiang University.

My research interests include 3D/4D Vision and Generative Models. Feel free to contact if you have anything to discuss!

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Research

ICLR 2025
arXiv / Project Page / Github / Demo (Depth) / Demo (Normal) / ComfyUI

Lotus is a diffusion-based visual foundation model with a simple yet effective adaptation protocol, aiming to fully leverage the pre-trained diffusion's powerful visual priors for dense prediction. With minimal training data, Lotus achieves SoTA performance in two key geometry perception tasks, i.e., zero-shot monocular depth and normal estimation.

ICLR 2025
arXiv / Project Page / Github / Demo (Soon)

Characterized by its emphasis on the interpretation of subject-essential attributes, the proposed DisEnvisioner effectively identifies and enhances the subject-essential feature while filtering out other irrelevant information, enabling exceptional image customization without cumbersome tuning or relying on multiple reference images.

SIGGRAPH Asia 2024
Paper / Video

DIScene is capable of generating complex 3D scene with decoupled objects and clear interactions. Leveraging a learnable Scene Graph and Hybrid Mesh-Gaussian representation, we get 3D scenes with superior quality. DIScene can also flexibly edit the 3D scene by changing interactive objects or their attributes, benefiting diverse applications.

ECCV 2024
arXiv / Paper / Project Page

We introduce Bi-TTA, a method that leverages spatial and temporal consistency for appropriate self-supervision, coupled with novel prospective and retrospective adaptation strategies, enabling superior adaptation ability of pre-trained rPPG models to the target domain using only unannotated, instance-level target data.

CVPR 2024 Highlight
arXiv / Github / Demo / Video

We present LucidDreamer, a text-to-3D generation framework, to distill high-fidelity textures and shapes from pretrained 2D diffusion models with a novel Interval Score Matching objective and an advanced 3D distillation pipeline. Together, we achieve superior 3D generation results with photorealistic quality in a short training time.

Education

Experience

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