Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Creating plausible motions for a diverse range of characters is a long-standing goal in computer graphics. Current learningbased motion synthesis methods rely on large-scale motion datasets, which are often difficult if not impossible to acquire. On the other hand, pose data is more accessible, since static posed characters are easier to create and can even be extracted from images using recent advancements in computer vision. In this paper, we tap into this alternative data source and introduce a neural motion synthesis approach through retargeting, which generates plausible motion of various characters that only have pose data by transferring motion from one single existing motion capture dataset of another drastically different characters. Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images. Additionally, a conducted user study indicated that a majority of participants found our retargeted motion to be more enjoyable to watch, more lifelike in appearance, and exhibiting fewer artifacts. Our code and dataset can be accessed here.
Description
CCS Concepts: Computing methodologies → Motion processing
@article{10.1111:cgf.15170,
journal = {Computer Graphics Forum},
title = {{Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior}},
author = {Zhao, Qingqing and Li, Peizhuo and Yifan, Wang and Sorkine-Hornung, Olga and Wetzstein, Gordon},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15170}
}