384 citations · 616 across the 9 of their papers we have counts for
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cs.GR2023★ 22 cited
AdaptNet: Policy Adaptation for Physics-Based Character Control
Pei Xu, Kaixiang Xie, Sheldon Andrews +5
Motivated by humans' ability to adapt skills in the learning of new ones, this paper presents AdaptNet, an approach for modifying the latent space of existing policies to allow new…
cs.GR2023★ 27 cited
Composite Motion Learning with Task Control
Pei Xu, Xiumin Shang, Victor Zordan +1
We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforceme…
cs.GR2021★ 27 cited
A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control
Pei Xu, Ioannis Karamouzas
We present a simple and intuitive approach for interactive control of physically simulated characters. Our work builds upon generative adversarial networks (GAN) and reinforcement…