Stylistic Locomotion Modeling with Conditional Variational Autoencoder
| dc.contributor.author | Du, Han | en_US |
| dc.contributor.author | Herrmann, Erik | en_US |
| dc.contributor.author | Sprenger, Janis | en_US |
| dc.contributor.author | Cheema, Noshaba | en_US |
| dc.contributor.author | hosseini, somayeh | en_US |
| dc.contributor.author | Fischer, Klaus | en_US |
| dc.contributor.author | Slusallek, Philipp | en_US |
| dc.contributor.editor | Cignoni, Paolo and Miguel, Eder | en_US |
| dc.date.accessioned | 2019-05-05T17:49:41Z | |
| dc.date.available | 2019-05-05T17:49:41Z | |
| dc.date.issued | 2019 | |
| dc.description.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. | en_US |
| dc.description.sectionheaders | Animation and Simulation | |
| dc.description.seriesinformation | Eurographics 2019 - Short Papers | |
| dc.identifier.doi | 10.2312/egs.20191002 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 9-12 | |
| dc.identifier.uri | https://doi.org/10.2312/egs.20191002 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/egs20191002 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Animation | |
| dc.subject | Motion processing | |
| dc.title | Stylistic Locomotion Modeling with Conditional Variational Autoencoder | en_US |