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We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs).
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Generative adversarial networks
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Improved techniques for training gans
Salimans, T., Goodfellow, I.J., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 2016
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HP-GAN: probabilistic 3d human motion prediction via GAN
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Language generation with recurrent generative adversarial networks without pre-training
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A recurrent variational autoencoder for human motion synthesis
Komura, T., Habibie, I., Holden, D., Schwarz, J., Yearsley, J.: · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: · 2017
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Wasserstein gan
Arjovsky, M., Chintala, S., Bottou1, L.: · 2017
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Learning human motion models for long-term predictions
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Martinez, J., Black, M.J., Romero, J.: · 2017
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Globally and locally consistent image completion
Iizuka, S., Simo-Serra, E., Ishikawa, H.: · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
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Liu, M.Y., Breuel, T., Kautz, J.: · 2017
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Deep representation learning for human motion prediction and classification
Bütepage, J., Black, M.J., Kragic, D., Kjellström, H.: · 2017
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Auto-conditioned LSTM network for extended complex human motion synthesis
Li, Z., Zhou, Y., Xiao, S., He, C., Li, H.: · 2018
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CMU graphics lab motion capture database
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Boundary-seeking generative adversarial networks
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CMU graphics lab motion capture database Motionbuilder-friendly BVH conversion
Hahn, B.: · 2018
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A note on the inception score
Barratt, S., Sharma, R.: · 2018
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