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Data-driven modelling and synthesis of motion is an active research area with applications that include animation, games, and social robotics.
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Chuang Ding, Pengcheng Zhu, and Lei Xie. 2015 · 2015
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Which training methods for GANs do actually converge?. In Proceedings of the International Conference on Machine Learning (ICML’18) . PMLR, 3481–3490
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Hai X. Pham, Yuting Wang, and Vladimir Pavlovic. 2018 · 2018
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Learning motion manifolds with convolutional autoencoders. In SIGGRAPH Asia 2015 Technical Briefs (SA’15) . ACM, New York, NY, USA, Article 18, 4 pages
Daniel Holden, Jun Saito, Taku Komura, and Thomas Joyce. 2015 · 2015
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Sergey Ioffe and Christian Szegedy. 2015 · 2015
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Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Ian Goodfellow. 2016 · 2016
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Minimum entropy rate simplification of stochastic processes
Gustav Eje Henter and W. Bastiaan Kleijn. 2016 · 2016
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beta-VAE: Learning basic visual concepts with a constrained variational framework. In Proceedings of the International Conference on Learning Representations (ICLR’16) . 22
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2016 · 2016
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GANimation: Anatomically-aware facial animation from a single image. In Proceedings of the European Conference on Computer Vision (ECCV’18) . Springer, Cham, Switzerland, 835–851
Albert Pumarola, Antonio Agudo, Aleix M. Martinez, Alberto Sanfeliu, and Francesc Moreno-Noguer. 2018 · 2018
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Novel realizations of speech-driven head movements with generative adversarial networks. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’18) . IEEE Signal Processing Society, Piscataway, NJ, USA, 6169–6173
Najmeh Sadoughi and Carlos Busso. 2018 · 2018
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Efficiently trainable text-to-speech system based on deep convolutional networks with guided attention. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’18) . IEEE Signal Processing Society, Piscataway, NJ, USA, 4784–4788
Hideyuki Tachibana, Katsuya Uenoyama, and Shunsuke Aihara. 2018 · 2018
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End-to-end speech-driven facial animation with temporal GANs. In Proceedings of the British Machine Vision Conference (BMVC’18) . BMVA Press, Durham, UK, 12
Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. 2018 · 2018
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Autoregressive neural f0 model for statistical parametric speech synthesis
Xin Wang, Shinji Takaki, and Junichi Yamagishi. 2018 · 2018
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Mode-adaptive neural networks for quadruped motion control
He Zhang, Sebastian Starke, Taku Komura, and Jun Saito. 2018 · 2018
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Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, and Hao Li. 2018 · 2018
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Large scale GAN training for high fidelity natural image synthesis. In Proceedings of the International Conference on Learning Representations (ICLR’19) . 35
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019 · 2019
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Multi-objective adversarial gesture generation. In Proceedings of the ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG’19) . ACM, New York, NY, USA, Article 3, 10 pages
Ylva Ferstl, Michael Neff, and Rachel McDonnell. 2019 · 2019
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FloWaveNet: A generative flow for raw audio. In Proceedings of the International Conference on Machine Learning (ICML’19) . PMLR, 3370–3378
Sungwon Kim, Sang-Gil Lee, Jongyoon Song, Jaehyeon Kim, and Sungroh Yoon. 2019 · 2019
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Analyzing input and output representations for speech-driven gesture generation. In Proceedings of the ACM International Conference on Intelligent Virtual Agents (IVA’19) . ACM, New York, NY, USA, 97–104
Taras Kucherenko, Dai Hasegawa, Gustav Eje Henter, Naoshi Kaneko, and Hedvig Kjellström. 2019 · 2019
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Do deep generative models know what they don’t know?. In Proceedings of the International Conference on Learning Representations (ICLR’19) . 19
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan. 2019 · 2019
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WaveGlow: A flow-based generative network for speech synthesis. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’19) . IEEE Signal Processing Society, Piscataway, NJ, USA, 3617–3621
Ryan Prenger, Rafael Valle, and Bryan Catanzaro. 2019 · 2019
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Variational autoencoders are not autoencoders
Paul Rubenstein. 2019 · 2019
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Speech-driven animation with meaningful behaviors
Najmeh Sadoughi and Carlos Busso. 2019 · 2019
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Efficient neural networks for real-time motion style transfer
Harrison Jesse Smith, Chen Cao, Michael Neff, and Yingying Wang. 2019 · 2019
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Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control
Zhiyong Wang, Jinxiang Chai, and Shihong Xia. 2019 · 2019
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Robots learn social skills: End-to-end learning of co-speech gesture generation for humanoid robots. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’19) . IEEE Robotics and Automation Society, Piscataway, NJ, USA, 4303–4309
Youngwoo Yoon, Woo-Ri Ko, Minsu Jang, Jaeyeon Lee, Jaehong Kim, and Geehyuk Lee. 2019 · 2019
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Fixup initialization: Residual learning without normalization. In Proceedings of the International Conference on Learning Representations (ICLR’19) . 16
Hongyi Zhang, Yann N. Dauphin, and Tengyu Ma. 2019 · 2019
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Style-controllable speech-driven gesture synthesis using normalising flows
Simon Alexanderson, Gustav Eje Henter, Taras Kucherenko, and Jonas Beskow. 2020 · 2020
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What is the state of neural network pruning?. In Proceedings of the Conference on Machine Learning and Systems (MLSys’20) . 129–146
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag. 2020 · 2020
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Robust motion in-betweening
Félix G. Harvey, Mike Yurick, Derek Nowrouzezahrai, and Christopher Pal. 2020 · 2020
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Probability distillation: A caveat and alternatives. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI’20, Vol. 115) . PMLR, 1212–1221
Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, and Aaron Courville. 2020 · 2020
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VideoFlow: A conditional flow-based model for stochastic video generation. In Proceedings of the International Conference on Learning Representations (ICLR’20) . 18
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Durk Kingma. 2020 · 2020
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Character controllers using motion VAEs
Hung Yu Ling, Fabio Zinno, George Cheng, and Michiel van de Panne. 2020 · 2020
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Local motion phases for learning multi-contact character movements
Sebastian Starke, Yiwei Zhao, Taku Komura, and Kazi Zaman. 2020 · 2020
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Realistic speech-driven facial animation with GANs
Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. 2020 · 2020
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Neural autoregressive flows. In Proceedings of the International Conference on Machine Learning (ICML’18) . PMLR, 2078–2087
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville. 2018 · 2087
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