Stylistic Locomotion Modeling with Conditional Variational Autoencoder
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.
BibTeX
@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},
DOI = {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},
DOI = {10.2312/egs.20191002}
}