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Method
Our method consists of three steps. First, we divide a motion dataset into tasks and train an RL policy for each task. Next, we use the expert policies to collect "noisy-state clean-action" trajectories, where the noisy state is obtained by executing actions from the RL policy with added noise, and the clean action is simply the action from the RL policy without noise. The clean action can be thought of as a corrective action from a noisy state. Lastly, we use the resulting dataset to train a diffusion policy using supervised learning.
Results
Perturbation Recovery
Diffusion Policy captures the multimodality of perturbation recoverty strategies. Given the same perturbation, the character can recover in multiple ways. Capturing this multimodality allows for increased robustness to out-of-distribution perturbations.
Motion Tracking
PDP is capable of tracking a wide range of dynamic motions. We successfully track 98.9% of all AMASS* motions. *Dataset does not contain motions that are infeasible in our simulator, such as those involving object interactions.
Walk
Squat
Kick
Handstand
Breakdance
Backflip
Text-to-Motion
PDP is capable of generating diverse and realistic human motion from text descriptions. We can also chain together text commands to synthesize novel motion sequences.
"A person walks clockwise in a circle"
"Kneel down on the ground"
"A person jumps in the air"
BibTeX
@inproceedings{10.1145/3680528.3687683,
author = {Truong, Takara Everest and Piseno, Michael and Xie, Zhaoming and Liu, Karen},
title = {PDP: Physics-Based Character Animation via Diffusion Policy},
year = {2024},
isbn = {9798400711312},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3680528.3687683},
doi = {10.1145/3680528.3687683},
articleno = {86},
numpages = {10},
keywords = {character animation, reinforcement learning, diffusion models},
series = {SA '24}
}