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Style Invariant Locomotion Classification for Character Control
Author(s) -
Boehs G.E.,
Vieira M.L.H.
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13590
Subject(s) - stylized fact , computer science , character (mathematics) , invariant (physics) , artificial intelligence , style (visual arts) , control (management) , artificial neural network , motion capture , motion (physics) , human–computer interaction , mathematics , history , geometry , archaeology , economics , mathematical physics , macroeconomics
We present a real‐time system for character control that relies on the classification of locomotive actions in skeletal motion capture data. Our method is both progress dependent and style invariant. Two deep neural networks are used to correlate body shape and implicit dynamics to locomotive types and their respective progress. In comparison to related work, our approach does not require a setup step and enables the user to act in a natural, unconstrained manner. Also, our method displays better performance than the related work in scenarios where the actor performs sharp changes in direction and highly stylized motions while maintaining at least as good performance in other scenarios. Our motivation is to enable character control of non‐bipedal characters in virtual production and live immersive experiences, where mannerisms in the actor's performance may be an issue for previous methods.