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    Stylistic Locomotion Modeling with Conditional Variational Autoencoder

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    Date
    2019
    Author
    Du, Han ORCID
    Herrmann, Erik ORCID
    Sprenger, Janis ORCID
    Cheema, Noshaba ORCID
    hosseini, somayeh ORCID
    Fischer, Klaus ORCID
    Slusallek, Philipp ORCID
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    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}
    }
    URI
    https://doi.org/10.2312/egs.20191002
    https://diglib.eg.org:443/handle/10.2312/egs20191002
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA