Lei Zhong

I'm a PhD student in the School of Informatics at the University of Edinburgh, supervised by Prof. Changjian Li. Before that, I received a Master's degree from Nankai University, supervised by Prof. Shao-ping Lu, and a Bachelor's degree from Southwest University.

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Research

My research interests lie at the intersection of vision and graphics. Recently, I have been focusing on 3D human motion modeling and generation. Some papers are highlighted.

— Preprints —
MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow
Zhe Li, Yisheng He, Lei Zhong, Weichao Shen, Qi Zuo, Lingteng Qiu, Zilong Dong, Laurence Tianruo Yang, Weihao Yuan,
Arxiv 2024
paper / project
— 2025 —
PoseTraj: Pose-Aware Trajectory Control in Video Diffusion
Longbin Ji, Lei Zhong, Wei Pengfei, Changjian Li,
CVPR 2025
paper / project

This paper introduces PoseTraj, which leverages a Two-Stage Pose-Aware Pretraining framework and a large-scale synthetic dataset to accurately model 6D object motion.

ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learning
Yi Yang, Lei Zhong, Huiping Zhuang,
WACV 2025
paper / project

— 2024 —
SMooDi: Stylized Motion Diffusion Model
Lei Zhong, Yiming Xie, Varun Jampani, Deqing Sun, Huaizu Jiang
ECCV 2024
paper / project / code

This paper introduces SMooDi, which enables stylized motion generation by incorporating style motion sequences into a text-conditioned human motion generation model.

OmniControl: Control Any Joint at Any Time for Human Motion Generation
Yiming Xie, Varun Jampani, Lei Zhong, Deqing Sun, Huaizu Jiang
ICLR 2024
paper / project / code

This paper introduces OmniControl, which incorporates flexible spatial control signals into a text-conditioned human motion generation model based on the diffusion process.

— Previous —
Aesthetic-guided Outward Image Cropping
Lei Zhong, Feng-Heng Li, Hao-Zhi Huang, Yong Zhang, Shao-Ping Lu, Jue Wang
ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2021)
paper / slide

This paper proposes an aesthetic-guided outward image cropping method that extends beyond the image border to achieve compositions unattainable with previous methods.

A Graph-Structured Representation with BRNN for Static-based Facial Expression Recognition
Lei Zhong, Changmin Bai, Jianfeng Li, Tong Chen, Shigang Li, Yiguang Liu
FG 2019 (IEEE Conference on Automatic Face and Gesture Recognition)
paper


Design and source code from Jon Barron's website source code.