%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2018/08.26.13.19
%2 sid.inpe.br/sibgrapi/2018/08.26.13.19.10
%@doi 10.1109/SIBGRAPI.2018.00035
%T Decoupling Expressiveness and Body-Mechanics in Human Motion
%D 2018
%A Boehs, Gustavo Eggert,
%A Viera, Milton Luiz Horn,
%A Pereira, Clovis Geyer,
%@affiliation Universidade Federal de Santa Catarina
%@affiliation Universidade Federal de Santa Catarina
%@affiliation Universidade Federal de Santa Catarina
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%C Foz do Iguaçu, PR, Brazil
%8 29 Oct.-1 Nov. 2018
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K motion capture, deep learning, animation.
%X Modern motion capturing systems can accurately store human motion with high precision. Editing this kind of data is troublesome, due to the amount and complexity of data. In this paper, we present a method for decoupling the aspects of human motion that are strictly related to locomotion and balance, from other movements that may convey expressiveness and intentionality. We then demonstrate how this decoupling can be useful in creating variations of the original motion, or in mixing different actions together.
%@language en
%3 decoupling.pdf