Xue Bin (Jason) Peng – ACM SIGGRAPH HISTORY ARCHIVES

Xue Bin (Jason) Peng

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About Xue Bin (Jason) Peng

Affiliations
Simon Fraser University, Assistant Professor
Location
Burnaby, British Columbia, Canada
Website
https://xbpeng.github.io/
Bio

Excerpt from https://xbpeng.github.io/

Assistant Professor at Simon Fraser University (SFU) and a Research Scientist at NVIDIA. Received a Ph.D. from UC Berkeley, advised by Professor Sergey Levine and Professor Pieter Abbeel. Prior to that, received an M.Sc from the University of British Columbia, advised by Professor Michiel van de Panne. My work lies in the intersection between computer graphics and machine learning, with a focus on reinforcement learning for motion control of simulated characters. Previously worked for Sony, Google Brain, OpenAI, Adobe Research, Disney Research, Microsoft (343 Industries), and Capcom.

Conference Contributions

Presentations
2023 Technical Papers: Tessler_CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
CALM: Conditional Adversarial Latent Models  for Directable Virtual Characters
SIGGRAPH 2023
[Technical Papers]
Presenter(s):
2023 Technical Papers: Zhang_Learning Physically Simulated Tennis Skills from Broadcast Videos
Learning Physically Simulated Tennis Skills from Broadcast Videos
SIGGRAPH 2023
[Technical Papers]
Presenter(s):
2023 Technical Papers: Hassan_Synthesizing Physical Character-Scene Interactions
Synthesizing Physical Character-Scene Interactions
SIGGRAPH 2023
[Technical Papers]
Presenter(s):
2022 Technical Paper: Peng_ASE: large-scale reusable adversarial skill embeddings for physically simulated characters
ASE: large-scale reusable adversarial skill embeddings for physically simulated characters
SIGGRAPH 2022
[Technical Papers]
Presenter(s):
2021 Technical Paper: PENG_AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
AMP: adversarial motion priors for stylized physics-based character control
SIGGRAPH 2021
[Technical Papers]
Presenter(s):
2018 Technical Paper: PENG_DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
DeepMimic: example-guided deep reinforcement learning of physics-based character skills
SIGGRAPH 2018
[Technical Papers]
Presenter(s):
Entry #: 143
2017 Technical Paper: PENG_DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning
DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning
SIGGRAPH 2017
[Technical Papers]
Presenter(s):
2016 Technical Paper: Peng_Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning
Terrain-adaptive locomotion skills using deep reinforcement learning
SIGGRAPH 2016
[Technical Papers]
Presenter(s):
2015 Technical Paper: Peng_Dynamic Terrain Traversal Skills Using Reinforcement Learning
Dynamic terrain traversal skills using reinforcement learning
SIGGRAPH 2015
[Technical Papers]
Presenter(s):

Other Information

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