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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


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.