I am a postdoctoral researcher at The Movement Lab of Stanford University, working with Prof. C. Karen Liu. My research interests include artificial intelligence, computer graphics, and computer vision with a focus on motion planning and reinforcement learning for physics-based character control and embodied agent. I am also interested in applying computer science techniques to other disciplines, like bioengineering and biomedical engineering.
Before joining Stanford University, I was a research assistant professor at Clemson University, working at the Big Data Analytics Lab with Prof. Feng Luo. I received my Ph.D. in computer science from Clemson University under the supervision of Prof. Ioannis Karamouzas. Prior to that, I received an M.S. in electrical engineering from University of Minnesota at Twin Cities.
Publications
2024
SIGGRAPH Asia 2024
SIGGRAPH Asia 2024
FürElise: Capturing and Physically Synthesizing Hand Motions of Piano Performance
In SIGGRAPH Asia, 2024.
2023
SIGGRAPH Asia 2023
AdaptNet: Policy Adaptation for Physics-Based Character Control
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2023).
RA-L & ICRA 2024
Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction
IEEE Robotics and Automation Letters.
Also in IEEE International Conference on Robotics and Automation, 2024.
SIGGRAPH 2023
Composite Motion Learning with Task Control
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2023). In technical papers trailer.
SCA 2023
Too Stiff, Too Strong, Too Smart: Evaluating Fundamental Problems with Motion Control Policies
PACM on Computer Graphics and Interactive Techniques.
In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2023.
2022
ECCV 2022
SocialVAE: Human Trajectory Prediction using Timewise Latents
In the 17th European Conference on Computer Vision, 2022.
2021
MIG 2021
PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network
In ACM SIGGRAPH Conference on Motion, Interaction and Games, 2021. Best paper nomination.
Also in NeurIPS Deep Reinforcement Learning workshop, 2021.
IROS 2021
Human Inspired Multi-Agent Navigation using Knowledge Distillation
In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021.
SCA 2021
A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control
PACM on Computer Graphics and Interactive Techniques. Cover article.
In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2021.
Bioengineering & Biomedical Engineering
JCI Insight
Explainable Deep Learning and Biomechanical Modeling for TMJ Disorder Morphological Risk Factors, Shuchun Sun*, Pei Xu*, Nathan Buchweitz*, Cherice N Hill, Farhad Ahmadi, Marshall B Wilson, Angela Mei, Xin She, Benedikt Sagl, Elizabeth H Slate, Janice S Lee, Yongren Wu, Hai Yao, 2024.Osteoarthritis and Cartilage Open
Mask R-CNN Provides Efficient and Accurate Measurement of Chondrocyte Viability in the Label-Free Assessment of Articular Cartilage, Hongming Fan, Pei Xu, Xun Chen, Yang Li, Zhao Zhang, Jennifer Hsu, Michael Le, Emily Ye, Bruce Gao, Harry Demos, Hai Yao, Tong Ye, 2023.SPIE BiOS
Automated Chondrocyte Viability Analysis of Articular Cartilage based on Deep Learning Segmentation and Classification of Two-Photon Microscopic Images, Hongming Fan, Pei Xu, Michael Le, Jennifer Hsu, Xun Chen, Yang Li, Zhao Zhang, Bruce Gao, Shane Woolf, Tong Ye, 2022.
Prior Work
MIG 2019
Low Dimensional Motor Skill Learning Using Coactivation, Avinash Ranganath, Pei Xu, Ioannis Karamouzas, Victor Zordan.- Gesture-based Human-robot Interaction for Field Programmable Autonomous Underwater Robots, Pei Xu, 2017.
- A Real-Time Hand Gesture Recognition and Human-Computer Interaction System, Pei Xu, 2017.