Using LSTM for Automatic Classification of Human Motion Capture Data
Publication by
Rogerio E. da Silva,
Jan Ondrej,
Aljosa Smolic
Related to the Smart Asset re-Use in Creative Environments (SAUCE) project
Published in VISIGRAPP, 2019
Related to the Smart Asset re-Use in Creative Environments (SAUCE) project
Published in VISIGRAPP, 2019
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.