Zhengjie (Jerry) Xu

I am a third-year PhD student in Computer Science and Engineering at the University of Michigan, Ann Arbor. I am luckily advised by Prof. Stella Yu. I am broadly interested in computer vision and robotics.

Previously, I obtained my M.S.E. in Robotics from University of Pennsylvania, and B.S. in Computer Engineering and Applied Mathematics from UCSD. During my master, I was fortunate to work with Prof. Jianbo Shi on image generation and reconstruction. I also worked with Prof. Hao Su and Prof. Garrison W. Cottrell during my undergrad.

Email  /  GitHub  /  Google Scholar

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Research
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Unified Humanoid Fall-Safety Policy from a Few Demonstrations


Zhengjie Xu, Ye Li, Kwan-yee Lin, Stella X. Yu
In submission, 2025
arxiv / website /

We aim to go beyond keeping balance to make the entire fall-and-recovery process safe and autonomous: prevent falls when possible, reduce impact when unavoidable, and stand up when fallen. By fusing sparse human demonstrations with reinforcement learning and an adaptive diffusion-based memory of safe reactions, we learn whole-body behaviors that unify fall prevention, impact mitigation, and rapid recovery in one policy.
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Perceptual Artifacts Localization for Image Synthesis Tasks


Lingzhi Zhang*, Zhengjie Xu*, Connelly Barnes, Yuqian Zhou, Qing Liu, He Zhang, Zhe Lin, Eli Shechtman, Sohrab Amirghodsi, Jianbo Shi
ICCV, 2023
arxiv / code /

We generalize Perceptual Artifacts Localization to ten diverse image synthesis, and shows promising accuracy. We also show the effectiveness of automatic artifacts fixing and quality assessment as downstream applications.
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Optimizing Algorithms From Pairwise User Preferences


Lukas Zhornyak*, Zhengjie Xu*, Haoran Tang*, Jianbo Shi
arxiv, 2022
arxiv / code /

We present HashEncoding, a novel autoencoding architecture that leverages a non-parametric multiscale coordinate hash function to facilitate a per-pixel decoder without convolutions. By leveraging the space-folding behaviour of hashing functions, HashEncoding allows for an inherently multiscale embedding space that remains much smaller than the original image.
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Generalization in Cardiac Image Segmentation


Zhengjie Xu, Zixiang Zhou, Garrison W. Cottrell, and Mai H. Nguyen
IEEE International Conference on Big Data, 2021
arxiv /

We focus on the task of cardiac image semantic segmentation and synthesize five different real-world scenarios to find the optimal training approach for deep learning models to achieve good generalization across different datasets.



Other Projects

These include coursework, side projects and unpublished research work.

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Super-pixel Sampling in 3D


UCSD CSE 291: Deep Learning meets Geometry
2020-04-01
paper /

We implemented a differentiable grouping module and combine it with off-the-shelf point cloud analysis frameworks (PointNet++). The module optimizes the embedding space to enforce the local feature consistency. The learnt mid-level abstraction can be used to discover geometry primitives as well as simplify the downstream tasks. With this module, we out-performed the original framework in point segmentation task.

Teaching

I was a TA/GSI for the following courses:

  • UPenn CIS 6800: Advanced Topics in Machine Perception (Fall 2022)
  • UM EECS 542: Advanced Topics in Computer Vision (Fall 2024)
  • UM EECS 442: Computer Vision (Winter 2025)

Misc

I have a lovely ragdoll as you can see from my profile photo. His name is Dodo(代代) and he is now 4 years old!


Design and source code from Jon Barron's website