Survey on Style in 3D Human Body Motion: Taxonomy, Data, Recognition and Its Applications

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.