Dongxu Wei (韦东旭)

I am currently a Postdoctoral Researcher at Spatial Intelligence and Robotics Lab, Westlake University, led by Prof. Peidong Liu (刘沛东). Before that, I worked at City Brain Lab, Alibaba DAMO Academy as a Senior Algorithm Engineer.

I obtained my B.S. degree from Harbin Institute of Technology in 2017, and obtained my Ph.D. degree from Zhejiang University in 2022. My doctoral advisor is Prof. Haibin Shen (沈海斌).

My research focuses on Spatial Intelligence, especially 3D-Grounded Representation, World Model, and World Action Model. I have published papers in top-tier venues such as CVPR, NeurIPS, AAAI, ACM MM, TMM, and TCSVT.

🔥 I am looking for research or industry positions in the field of Spatial Intelligence, especially in areas related to World Models, World Action Models and 3D AIGC. Feel free to reach out!

Email  |  Google Scholar  |  GitHub  |  RedNote

headshot
Research

Equal Contribution *, Corresponding Author ✉️, Project Lead †

Any 3D Scene is Worth 1K Tokens: 3D-Grounded Representation for Scene Generation at Scale
Dongxu Wei *, Qi Xu *, Zhiqi Li, Hangning Zhou †, Cong Qiu, Hailong Qin, Mu Yang, Zhaopeng Cui, Peidong Liu ✉️
arXiv 2026
project page / arXiv / code

Represent 3D scenes with fixed-length 3D latent tokens and perform diffusion modeling directly within 3D latent space.

SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
Qi Xu *, Dongxu Wei * ✉️, Lingzhe Zhao, Wenpu Li, Zhangchi Huang, Shunping Ji, Peidong Liu ✉️
Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. Spotlight paper, top 3%
project page / arXiv / code

The first alignment-free method for unified 3D reconstruction and understanding.

Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
Dongxu Wei, Zhiqi Li, Peidong Liu ✉️
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025
project page / arXiv / code

Unify pixel-based and volume-based Gaussians within a single framework, and make them complement each other for ego-centric and large-scale 3D scene reconstruction.

C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer
Dongxu Wei, Xiaowei Xu, Haibin Shen, Kejie Huang ✉️
AAAI Conference on Artificial Intelligence (AAAI), 2021. Oral presentation.
arXiv / code

Warp images with estimated flows rather than generate videos from scratch for spatial-temporal consistent motion transfer.

GAC-GAN: A General Method for Appearance-Controllable Human Video Motion Transfer
Dongxu Wei, Xiaowei Xu, Haibin Shen, Kejie Huang ✉️
IEEE Transactions on Multimedia (TMM), 2020.
arXiv / code

A two-stage human video generation method with general-purpose and part-level controllability.

RobTrack: A Robust Tracker Baseline towards Real-World Robustness in Multi-Object Tracking and Segmentation
Dongxu Wei, Jiashen Hua, Hualiang Wang, Baisheng Lai, Kejie Huang, Chang Zhou, Jianqiang Huang, Xiansheng Hua
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021 | paper

🏆 The 1st-place solution for the CVPR 2021 Robust Multi-Object Tracking and Segmentation (RobMOTS) Challenge.

OAW-GAN: Occlusion-Aware Warping GAN for Unified Human Video Synthesis
Dongxu Wei, Kejie Huang ✉️, Liyuan Ma, Jiashen Hua, Baisheng Lai, Haibin Shen
Applied Intelligence, 2023 | paper

A unified framework that can achieve human motion transfer, attribute editing and texture inpainting.

FDA-GAN: Flow-based Dual Attention GAN for Human Pose Transfer
Liyuan Ma, Kejie Huang ✉️, Dongxu Wei, Zhaoyan Ming, Haibin Shen
IEEE Transactions on Multimedia (TMM), 2021 | paper

Flow-based dual attention that enables deformable- and occlusion-aware fusion for enhancing fidelity of human image synthesis.

TDSD: Text-driven scene-decoupled weakly supervised video anomaly detection
Shengyang Sun, Jiashen Hua, Junyi Feng, Dongxu Wei, Baisheng Lai, Xiaojin Gong ✉️
ACM International Conference on Multimedia (ACMMM), 2024
paper / code

The first work to address scene-dependent video anomaly detection under a weakly supervised setting.

Delving Into Instance Modeling for Weakly Supervised Video Anomaly Detection
Shengyang Sun, Jiashen Hua, Junyi Feng, Dongxu Wei, Baisheng Lai, Xiaojin Gong ✉️
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2025 | paper

Formulate weakly-supervised video anomaly detection as a multi-instance modeling problem, and propose dynamic segment merging module and retrieval-augmented anomaly restoration module to tackle the problem from segment-level and feature-level, respectively.

Funding & Grants

I am currently leading an open project of the state key laboratory of CAD & CG at Zhejiang University, dedicated to solving various problems related to large-scale 3D scene generation.


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