Authors:
Rogério E. da Silva
1
;
Jan Ondřej
2
and
Aljosa Smolic
3
Affiliations:
1
V-SENSE, School of Computer Science and Statistics, Trinity College Dublin, Ireland, Department of Computer Science, Santa Catarina State University and Brazil
;
2
V-SENSE, School of Computer Science and Statistics, Trinity College Dublin, Ireland, Volograms, Dublin and Ireland
;
3
V-SENSE, School of Computer Science and Statistics, Trinity College Dublin and Ireland
Keyword(s):
Human Motion Classification, Motion Capture, Content Analysis, Deep Learning, Artificial Intelligence.
Related
Ontology
Subjects/Areas/Topics:
Animation Algorithms and Techniques
;
Animation and Simulation
;
Animation from Motion Capture
;
Animation Systems
;
Computer Vision, Visualization and Computer Graphics
;
Crowd Simulation
;
Human Figure Animation
Abstract:
Creative studios tend to produce an overwhelming amount of content everyday and being able to manage these data and reuse it in new productions represent a way for reducing costs and increasing productivity and profit. This work is part of a project aiming to develop reusable assets in creative productions. This paper describes our first attempt using deep learning to classify human motion from motion capture files. It relies on a long short-term memory network (LSTM) trained to recognize action on a simplified ontology of basic actions like walking, running or jumping. Our solution was able of recognizing several actions with an accuracy over 95% in the best cases.