Chen, W, Wang, H orcid.org/0000-0002-2281-5679, Yuan, Y et al. (2 more authors) (2020) Dynamic Future Net: Diversified Human Motion Generation. In: MM '20: Proceedings of the 28th ACM International Conference on Multimedia. ACM Multimedia 2020, 12-16 Oct 2020, Online. Association for Computing Machinery , pp. 2131-2139. ISBN 978-1-4503-7988-5
Abstract
Human motion modelling is crucial in many areas such as computergraphics, vision and virtual reality. Acquiring high-quality skele-tal motions is difficult due to the need for specialized equipmentand laborious manual post-posting, which necessitates maximiz-ing the use of existing data to synthesize new data. However, it is a challenge due to the intrinsic motion stochasticity of humanmotion dynamics, manifested in the short and long terms. In theshort term, there is strong randomness within a couple frames, e.g.one frame followed by multiple possible frames leading to differentmotion styles; while in the long term, there are non-deterministicaction transitions. In this paper, we present Dynamic Future Net,a new deep learning model where we explicitly focuses on the aforementioned motion stochasticity by constructing a generative model with non-trivial modelling capacity in temporal stochas-ticity. Given limited amounts of data, our model can generate a large number of high-quality motions with arbitrary duration, andvisually-convincing variations in both space and time. We evaluateour model on a wide range of motions and compare it with the state-of-the-art methods. Both qualitative and quantitative results show the superiority of our method, for its robustness, versatility and high-quality.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 ACM. This is an author produced version of a conference paper published in MM '20: Proceedings of the 28th ACM International Conference on Multimedia. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R031193/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jul 2020 15:03 |
Last Modified: | 26 Oct 2020 16:14 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/3394171.3413669 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163776 |