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http://dx.doi.org/10.15701/kcgs.2021.27.5.33

Motion Style Transfer using Variational Autoencoder  

Ahn, Jewon (Dept. of Intelligence Convergence, Hanyang University)
Kwon, Taesoo (Dept. of Computer and Software, Hanyang University)
Publication Information
Journal of the Korea Computer Graphics Society / v.27, no.5, 2021 , pp. 33-43 More about this Journal
Abstract
In this paper, we propose a framework that transfers the information of style motions to content motions based on a variational autoencoder network combined with a style encoding in the latent space. Because we transfer a style to a content motion that is sampled from a variational autoencoder, we can increase the diversity of existing motion data. In addition, we can improve the unnatural motions caused by decoding a new latent variable from style transfer. That improvement was achieved by additionally using the velocity information of motions when generating next frames.
Keywords
variational autoencoder; style autoencoder; motion style transfer; generative models; velocity integrate;
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