Stylistic Locomotion Modeling with Conditional Variational Autoencoder

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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We propose a novel approach to create generative models for distinctive stylistic locomotion synthesis. The approach is inspired by the observation that human styles can be easily distinguished from a few examples. However, learning a generative model for natural human motions which display huge amounts of variations and randomness would require a lot of training data. Furthermore, it would require considerable efforts to create such a large motion database for each style. We propose a generative model to combine the large variation in a neutral motion database and style information from a limited number of examples. We formulate the stylistic motion modeling task as a conditional distribution learning problem. Style transfer is implicitly applied during the model learning process. A conditional variational autoencoder (CVAE) is applied to learn the distribution and stylistic examples are used as constraints. We demonstrate that our approach can generate any number of natural-looking human motions with a similar style to the target given a few style examples and a neutral motion database.
Description

        
@inproceedings{
10.2312:egs.20191002
, booktitle = {
Eurographics 2019 - Short Papers
}, editor = {
Cignoni, Paolo and Miguel, Eder
}, title = {{
Stylistic Locomotion Modeling with Conditional Variational Autoencoder
}}, author = {
Du, Han
and
Herrmann, Erik
and
Sprenger, Janis
and
Cheema, Noshaba
and
hosseini, somayeh
and
Fischer, Klaus
and
Slusallek, Philipp
}, year = {
2019
}, publisher = {
The Eurographics Association
}, ISSN = {
1017-4656
}, ISBN = {}, DOI = {
10.2312/egs.20191002
} }
Citation