Title:
Human Motion Simulation and Human Action Corpus.

Abstract:
Acquisition of large scale good quality training samples is becoming a major issue in machine learning based human 
motion analysis. This research presents a method to simulate continuous gross human body motion with the intention 
to establish a human motion corpus for learning and recognition. The simulation is achieved by a 
temporal-spatial-temporal decomposition of human motion into actions, joint actions and actionlets based on 
the human kinematic model. The actionlet models the primitive moving phase of a joint and represents the muscle 
movement governed by kinesiological principles. Joint actions and body actions are constructed from actionlets 
through constrained concatenation and synchronization. Methods for concatenation and synchronization are proposed 
in this research. An action corpus with small number of action vocabularies is created to verify the feasibility 
of the proposed method.