Human Motion Generative Model Using Variational Autoencoder
Yuichiro Motegi, Yuma Hijioka, Makoto Murakami · International Journal of Modeling and Optimization · 2018
We present a technique to learn large human motion data captured with optical motion capture system, represent it in a low dimensional latent space, so as to generate natural and various human motions from it.To extract human motion features we use a convolutional autoencoder, and to represent the extracted features as a probability density function in a latent space we use a variational autoencoder.Motion generator is modeled as a map from a latent variable sampled in the latent space to a motion capture data.We stack the convolutional decoder on top of the variational decoder, which can sample a latent variable and produce a motion.As a result, our system can generate natural and various human motions from a 32-dimensional latent space.