Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
Qingqing Zhao Peizhuo Li Wang Yifan Olga Sorkine-Hornung Gordon Wetzstein
Creating believable motions for various characters has long been a goal in
computer graphics. Current learning-based motion synthesis methods depend on
extensive motion datasets, which are often challenging, if not impossible, to
obtain. 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 utilize this
alternative data source and introduce a neural motion synthesis approach
through retargeting. Our method generates plausible motions for characters that
have only pose data by transferring motion from an existing motion capture
dataset of another character, which can have drastically different skeletons.
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. Project page:
https://cyanzhao42.github.io/pose2motion