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Due to the emergence of Generative Adversarial Networks, video synthesis has witnessed exceptional breakthroughs.
Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
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Perception of human motion with different geometric models
Hodgins, J.K., O’Brien, J.F., Tumblin, J.: · 1998
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: · 2004
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Actions as space-time shapes
Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: · 2005
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Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.E.: · 2010
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The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression
Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: · 2010
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A.R., Shah, M.: · 2012
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Auto-encoding variational bayes
Kingma, D.P., Welling, M.: · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
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Human action recognition by representing 3d skeletons as points in a lie group
Vemulapalli, R., Arrate, F., Chellappa, R.: · 2014
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Conditional generative adversarial nets
Mirza, M., Osindero, S.: · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
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Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., Salakhudinov, R.: · 2015
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Action-conditional video prediction using deep networks in atari games
Oh, J., Guo, X., Lee, H., Lewis, R.L., Singh, S.: · 2015
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Hierarchical recurrent neural network for skeleton based action recognition
Du, Y., Wang, W., Wang, L.: · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2015
Cited alongside, same era.
Recurrent network models for human dynamics
Fragkiadaki, K., Levine, S., Felsen, P., Malik, J.: · 2015
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction
Finn, C., Goodfellow, I., Levine, S.: · 2016
Cited alongside, same era.
Deep multi-scale video prediction beyond mean square error
Mathieu, M., Couprie, C., LeCun, Y.: · 2016
Cited alongside, same era.
Generating videos with scene dynamics
Vondrick, C., Pirsiavash, H., Torralba, A.: · 2016
Cited alongside, same era.
Realtime multi-person 2d pose estimation using part affinity fields
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: · 2017
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Arjovsky, M., Chintala, S., Bottou, L.: · 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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Pose guided person image generation
Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., Van Gool, L.: · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., Metaxas, D.: · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Learning temporal transformations from time-lapse videos
Zhou, Y., Berg, T.L.: · 2016
Cited alongside, same era.
Instance normalization: The missing ingredient for fast stylization
TDmitry Ulyanov, Andrea Vedaldi, V.L.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 2016
Cited alongside, same era.
Unsupervised learning of disentangled representations from video
Denton, E.L., et al.: · 2017
Cited alongside, same era.
Temporal generative adversarial nets with singular value clipping
Saito, M., Matsumoto, E., Saito, S.: · 2017
Cited alongside, same era.
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
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Dualgan: Unsupervised dual learning for image-to-image translation
Yi, Z., Zhang, H., Tan, P., Gong, M.: · 2017
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Learning to discover cross-domain relations with generative adversarial networks
Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: · 2017
Later among the works it cites.
Dual motion gan for future-flow embedded video prediction
Liang, X., Lee, L., Dai, W., Xing, E.P.: · 2017
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2017
Later among the works it cites.
Spatial temporal graph convolutional networks for skeleton-based action recognition
Yan, S., Xiong, Y., Lin, D.: · 2018
Closest in time.
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: · 2018
Closest in time.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: · 2018
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