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Xuhai Chen
I received my M.S. degree from Zhejiang University, China, in 2026, where I was advised by Prof. Yong Liu. Prior to that, I received my bachelor's degree from Tongji University, China.
In 2024, I interned at the State Key Laboratory of CAD&CG at Zhejiang University, where I worked on motion generation with Prof. Xiaowei Zhou and Prof. Sida Peng.
In 2025, I worked as a remote research intern at the University of Pennsylvania, focusing on 3D reconstruction and generation with Prof. Lingjie Liu.
Since July 2026, I have been collaborating remotely with Prof. Guandao Yang at UT Austin on 3D mesh generation and editing.
My research interests lie in 3D computer vision and graphics, with a particular focus on 3D generative modeling, reconstruction, and editing. I have also worked on image super-resolution and anomaly detection.
I enjoy exploring new experiences and challenges, which makes me feel happy and fulfilled. Life is meant to be experienced.
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CV
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[2023.06]: CVPR 2023 workshop VAND Challenge: Winner in the Zero-shot Track, Honorable Mention in the Few-shot Track.
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Contact Matrix: Enhancing Dance Motion Synthesis with Precise Interaction Modeling
Xuhai Chen,
Zhi Cen,
Huaijin Pi,
Sida Peng,
Xiaowei Zhou,
Yong Liu
CVPRF, 2026
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Generate realistic reactive motions for duet dance by modeling fine-grained leader–follower contacts with contact-guided diffusion.
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Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Xuhai Chen,
Jiangning Zhang,
Chao Xu,
Yabiao Wang,
Chengjie Wang,
Yong Liu
CVPR, 2023
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Estimating space-variant blur degradation with the help of semantic information.
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A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD
Xuhai Chen,
Yue Han,
Jiangning Zhang
arXiv, 2023
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Technical report for the VAND challenge at the 2023 CVPR workshop.
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CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection
Xuhai Chen,
Jiangning Zhang,
Guanzhong Tian,
Haoyang He,
Wuhao Zhang,
Yabiao Wang,
Chengjie Wang,
Yong Liu
arXiv, 2023
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Adapt the CLIP model for anomaly segmentation by merely fine-tuning a linear layer, and explain the text prompts design from a distributional perspective.
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Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
Jiangning Zhang,
Xuhai Chen,
Yabiao Wang,
Chengjie Wang,
Yong Liu,
Xiangtai Li,
Ming-Hsuan Yang,
Dacheng Tao
arXiv, 2023
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Construct a reverse distillation architecture for multi-class anomaly detection using plain ViT.
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