Unpaired Motion Style Transfer from Video to Animation

Understand

Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation.

• Most existing data-driven approaches are supervised and rely on paired data, where motions with the same content are performed in different styles.
• In addition, these approaches are limited to transfer of styles that were seen during training.
• In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training.