Abstract
We propose a new statistical modeling approach that we call Sequential Adversarial Auto-encoder (SAAE) for learning a synthesis model for motion sequences. This model exploits the adversarial idea that has been popularized in the machine learning field for learning accurate generative models. We further propose a conditional variant of this model that takes as input an additional information such as the activity which is performed in a sequence, or the emotion with which it is performed, and which allows to perform synthesis in context.
We are very grateful to Catherine Pélachaud for fruitful discussion and for access to and help with the Emilya dataset
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Denton, E., Chintala, S., Szlam, A., Fergus, R.: Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. Arxiv, pp. 1–10 (2015)
Ding, Y., Prepin, K., Huang, J., Pelachaud, C., Artières, T.: Laughter animation synthesis. In: AAMAS (2014)
Fourati, N., Pelachaud, C.: Emilya: emotional body expression in daily actions database. In: LREC, pp. 3486–3493 (2014)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27, pp. 2672–2680 (2014)
Grochow, K., Martin, S.L., Hertzmann, A., Popovic, Z.: Style-based inverse kinematics. ACM Transactions on Graphics 23(3), 522–531 (2004)
Hofer, G., Shimodaira, H.: Automatic head motion prediction from speech data. In: INTERSPEECH, pp. 722–725 (2007)
Holden, D., Saito, J., Komura, T.: A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics 35(4), 1–11 (2016)
Huang, J., Wang, Q., Fratarcangeli, M., Yan, K., Pelachaud, C.: Multi-variate gaussian-based inverse kinematics. In: Computer Graphics Forum (2017)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.: Adversarial Autoencoders, pp. 1–10 (2015). arXiv: http://arxiv.org/abs/1511.05644
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104–3112 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wang, Q., Artières, T. (2017). Motion Capture Synthesis with Adversarial Learning. In: Beskow, J., Peters, C., Castellano, G., O'Sullivan, C., Leite, I., Kopp, S. (eds) Intelligent Virtual Agents. IVA 2017. Lecture Notes in Computer Science(), vol 10498. Springer, Cham. https://doi.org/10.1007/978-3-319-67401-8_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-67401-8_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67400-1
Online ISBN: 978-3-319-67401-8
eBook Packages: Computer ScienceComputer Science (R0)