Short-term Inbetweening of 3D Human Motions

Abstract

Creating computer generated human animations without the use of motion capture technology is a tedious and time consuming activity. Although there are several publications regarding animation synthesis using data driven methods, not many are dedicated towards the task of inbetweening, which consists of generating transition movements between frames. A modified version of LSTM, called Recurrent Transition Network (RTN), solves the inbetweening task for walking motion based on ten initial frames and two final frames. In this work, we are interested on the short-term inbetweening task, where we need to use the least amount of frames to generate the missing frames for short-term transitions. We are also interested on different kinds of movements, such as martial arts and Indian dance. Thus, we adapt the Recurrent Transition Network (RTN) to require only the two firts frames and the last one, called ARTN, and propose a simple post processing method combining ARTN with linear interpolation, called ARTN+. The results show that the average error of ARTN+ is less than the average error of each method (RTN and interpolation) separately in the martial arts and Indian dance dataset.