Abstract
The meaning of the word style depends on its context. While actions have already been quite studied for a while, style in human body motion is a growing topic of interest. In the context of animation, style is crucial as it brings realism and expressiveness to the motion of a character. Even though it is undoubtedly a key element in motions, its definition and the use of the word style in itself, among research works, lack consensus. Achieving realistic motions is tedious. It requires either a large motion capture dataset or the considerable work of artist animators. The lack of consistent style data is thus a challenge. Stylistic motion generation is quite studied in order to overcome this issue. This paper focuses on the study of style in human body motion from 3D human body skeletal data. It establishes a taxonomy of definitions of style, describes the data that have been used up until now, introduces key notions about motion capture data as well as machine learning, and presents approaches about style recognition, person identification through their style and motion style generation.
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