I am a Ph.D. student at The Chinese University of Hong Kong, Shenzhen,
working on 3D computer vision and graphics, advised by Prof. HAN Xiaoguang.
Previously, I obtained my M.S. from National University of Singapore, advised by Prof. Robby T. Tan.
I received my Bachelor's degree from University of Electronic Science and Technology of China.
I believe simple is better than complex.
I (try to) practice Slow Science. Slow is faster than fast.
I expect to graduate in June 2027 and am actively seeking full-time research opportunities in 3D generation and related areas. Please feel free to get in touch.
My research focuses on 3D generation and reconstruction. At Tencent Hunyuan3D, I contribute to the development of native 3D generative foundation models, focusing on ultra-high-fidelity geometry generation. My prior work in human-centric 3D vision and graphics spans neural 3D representations, reconstruction, and controllable generation, with an emphasis on generalization and learning from imperfect or synthetic data at scale. I am also exploring agentic 3D modeling, with broader interests in world models and embodied intelligence.
Introduce an efficient Point-Image Transformer that fuses hierarchical 3D point features with multimodal attention to generate high-fidelity, animatable 3D humans from pose-free images.
Propose a hy-plane representation that combines the strengths of tri-plane and spherical tri-plane methods while overcoming their inherent limitations.
Integrate a custom ControlNet and dual appearance module to generate globally consistent 360-degree head views capable of high-quality free-viewpoint rendering for both realistic and stylized head images.
Achieve 3D full-head synthesis without mirroring-face and multiple-face artifacts via spherical tri-plane representation and view-image consistency loss.
A model that enables real-time and zero-shot attribute separation of a given real face, allowing attribute transfer and rendering at novel views without the aid of multi-view information.
Infer 3D face reconstruction in both image space and model space to achieve high robustness and accuracy.
Earlier Projects
Detailed 3D Face Reconstruction
Reconstruct a detailed 3D face from a single image through self-supervised learning and differentiable rendering-based optimization.
Cloth Simulation via Deep Learning
Use neural networks to represent cloth, external objects, and the environment, and conduct a learning-based cloth simulation.
Vehicle Trajectory Forecasting
Trajectory Forecasting with Neural Networks: An Empirical Evaluation and A New Hybrid Model TITS, 2020
Conduct the most comprehensive evaluation of various models proposed for time series data prediction on vehicle trajectory forecasting task, and propose a hybrid model that combines the merits of MLP and LSTM.
Bike Sharing System
Propose the first neural network-based framework integrated with fuzzy clustering for predicting per-station demand and status in bike-sharing systems, achieving state-of-the-art performance on the New York CitiBike dataset.
Undergraduate Thesis
Gradual Knowledge Distillation
Video-based Human Action Detection.
Undergraduate Degree Thesis, 2020
Gradually distill knowledge from higher-precision model to lower-precision and finally binary model to alleviate the performance deterioration in Quantization.
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