Content-Preserving Motion Stylization using Variational Autoencoder

Authors

Chen-Chieh Liao, Jong-Hwan Kim, Hideki Koike, and Dong-Hyun Hwang

NAVER Cloud and Tokyo Institute of Technology

Abstract

This work proposes a motion style transfer network that transfers motion style between different motion categories using variational autoencoders. The proposed network effectively transfers style among various motion categories and can create stylized motion unseen in the dataset. The network contains a content-conditioned module to preserve the characteristic of the content motion, which is important for real applications. We implement the network with variational autoencoders, which enable us to control the intensity of the style and mix different styles to enrich the motion diversity.