Xuebin (Jason) Peng
Most Recent Affiliation(s):
- University of California
Award(s):
Learning Category: Presentation(s):
Type: [Technical Papers]
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters Presenter(s): [Tessler] [Guo] [Mannor] [Chechik] [Peng]
[SIGGRAPH 2023]
Type: [Technical Papers]
Learning Physically Simulated Tennis Skills from Broadcast Videos Presenter(s): [Zhang] [Makoviychuk] [Yuan] [Guo] [Fidler] [Peng] [Fatahalian]
[SIGGRAPH 2023]
Type: [Technical Papers]
Synthesizing Physical Character-Scene Interactions Presenter(s): [Hassan] [Guo] [Wang] [Black] [Fidler] [Peng]
[SIGGRAPH 2023]
Type: [Technical Papers]
ASE: large-scale reusable adversarial skill embeddings for physically simulated characters Presenter(s): [Peng] [Guo] [Halper] [Levine] [Fidler]
[SIGGRAPH 2022]
Type: [Technical Papers]
AMP: adversarial motion priors for stylized physics-based character control Presenter(s): [Peng] [Ma] [Abbeel] [Levine] [Kanazawa]
[SIGGRAPH 2021]
Type: [Technical Papers]
DeepMimic: example-guided deep reinforcement learning of physics-based character skills Presenter(s): [Peng] [Abbeel] [Levine] [Panne]
Entry No.: [143]
[SIGGRAPH 2018]
Type: [Technical Papers]
DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning Presenter(s): [Peng] [Berseth] [Yin] [Panne]
[SIGGRAPH 2017]
Role(s):
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