Zeshi Yang (楊 澤世)
I am an independent researcher, focusing on developing cutting edge AIGC techniques for various motion generation tasks. I am particularly enthusiastic about combining control methods (especially RL) with deep learning to realize high-quality, controllable and explainable motion synthesis systems.
I got my PhD degree from Simon Fraser Univeristy in 2023, majoring in physics-based character animation, motion control and deep reinforcement learning. Before that, I got my bachelor's degree in applied physics from USTC in 2018.
Education & Career
B.S. in Applied Physics, University of Science and Technology of China, 2014 - 2018
Ph.D in Computer Science, Simon Fraser University, 2018 - 2023
Visiting Scholar, Peking Univerisity, China, 2021 Aug - 2022 Aug
Research Intern, LightSpeed Studios, Tencent America, 2022 Sep - 2023 May
Research Scientist, miHoYo, 2023 June - 2024 Sep
Publications
Real-time Diverse Motion In-betweening with Space-time Control
ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG 2024)
Learning-based 2D Irregular Shape Packing
ACM Transactions on Graphics, Volume 42, Article 6 (Proc. ACM SIGGRAPH Aisa 2023)
Acquiring Stylized Motor Skills for Physics-based Characters
PhD Thesis, Simon Fraser University, 2023
Learning to Use Chopsticks in Diverse Gripping Styles
ACM Transactions on Graphics, Volume 41, Issue 4, Article 95 (Proc. ACM SIGGRAPH 2022)
Efficient Hyperparameter Optimization for Physics-based Character Animation
ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games 2021 (I3D 2021)
Discovering Diverse Athletic Jumping Strategies
ACM Transactions on Graphics, Volume 40, Issue 4, Article 91 (Proc. ACM SIGGRAPH 2021)
Neural fidelity warping for efficient robot morphology design
2021 International Conference on Robotics and Automation (ICRA 2021)
Redirected Smooth Mappings for Multiuser Real Walking in Virtual Reality
ACM Transactions on Graphics, 38(5), 2019.(Presented at Siggraph Asia)