A Recurrent Variational Autoencoder for Human Motion Synthesis

Ikhsanul Habibie, Daniel Holden, Jonathan Schwarz, Joe Yearsley and Taku Komura

Abstract

We propose a novel generative model of human motion that can be trained using a large motion capture dataset, and allows users to produce animations from high-level control signals. As previous architectures struggle to predict motions far into the future due to the inherent ambiguity, we argue that a user-provided control signal is desirable for animators and greatly reduces the predictive error for long sequences. Thus, we formulate a framework which explicitly introduces an encoding of control signals into a variational inference framework trained to learn the manifold of human motion. As part of this framework, we formulate a prior on the latent space, which allows us to generate high-quality motion without providing frames from an existing sequence. We further model the sequential nature of the task by combining samples from a variational approximation to the intractable posterior with the control signal through a recurrent neural network (RNN) that synthesizes the motion. We show that our system can predict the movements of the human body over long horizons more accurately than state-of-the-art methods.

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DOI

10.5244/C.31.119
https://dx.doi.org/10.5244/C.31.119

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Bibtex

            @inproceedings{BMVC2017_119,
                title={A Recurrent Variational Autoencoder for Human Motion Synthesis},
                author={Ikhsanul Habibie, Daniel Holden, Jonathan Schwarz, Joe Yearsley and Taku Komura},
                year={2017},
                month={September},
                pages={119.1-119.12},
                articleno={119},
                numpages={12},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.119},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.119}
            }