  While existing motion style transfer methods are effective between two
motions with identical content, their performance significantly diminishes when
transferring style between motions with different contents. This challenge lies
in the lack of clear separation between content and style of a motion. To
tackle this challenge, we propose a novel motion style transformer that
effectively disentangles style from content and generates a plausible motion
with transferred style from a source motion. Our distinctive approach to
achieving the goal of disentanglement is twofold: (1) a new architecture for
motion style transformer with `part-attentive style modulator across body
parts' and `Siamese encoders that encode style and content features
separately'; (2) style disentanglement loss. Our method outperforms existing
methods and demonstrates exceptionally high quality, particularly in motion
pairs with different contents, without the need for heuristic post-processing.
Codes are available at https://github.com/Boeun-Kim/MoST.
