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We consider the task of generating diverse and novel videos from a single video sample.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A.R., Shah, M.: · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A.R., Shah, M.: · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D.P., Welling, M.: · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: · 2014
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2015
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E.L., Chintala, S., Szlam, A., Fergus, R.: · 2015
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: · 2015
Earlier work this paper cites.
Learning spatiotemporal features with 3d convolutional networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Earlier work this paper cites.
Generating videos with scene dynamics
Vondrick, C., Pirsiavash, H., Torralba, A.: · 2016
Earlier work this paper cites.
Improved variational inference with inverse autoregressive flow
Kingma, D.P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., Welling, M.: · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., kavukcuoglu, k., Vinyals, O., Graves, A.: · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
Oord, A.v.d., Kalchbrenner, N., Kavukcuoglu, K.: · 2016
Earlier work this paper cites.
Combining markov random fields and convolutional neural networks for image synthesis
Li, C., Wand, M.: · 2016
Cited alongside, same era.
Precomputed real-time texture synthesis with markovian generative adversarial networks
Li, C., Wand, M.: · 2016
Cited alongside, same era.
Youtube-8m: A large-scale video classification benchmark
Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., Vijayanarasimhan, S.: · 2016
Cited alongside, same era.
Temporal generative adversarial nets with singular value clipping
Saito, M., Matsumoto, E., Saito, S.: · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., Lerchner, A.: · 2017
Cited alongside, same era.
Alphagan: Generative adversarial networks for natural image matting
Lutz, S., Amplianitis, K., Smolic, A.: · 2018
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“zero-shot” super-resolution using deep internal learning
Shocher, A., Cohen, N., Irani, M.: · 2018
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Mocogan: Decomposing motion and content for video generation
Tulyakov, S., Liu, M.Y., Yang, X., Kautz, J.: · 2018
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Towards high resolution video generation with progressive growing of sliced wasserstein gans
Acharya, D., Huang, Z., Paudel, D.P., Van Gool, L.: · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: · 2018
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, D.N.: · 2017
Cited alongside, same era.
Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
Mescheder, L., Nowozin, S., Geiger, A.: · 2017
Cited alongside, same era.
Veegan: Reducing mode collapse in gans using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M.U., Sutton, C.: · 2017
Cited alongside, same era.
Ulyanov, D., Vedaldi, A., Lempitsky, V.: · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2017
Cited alongside, same era.
Improved training of wasserstein gans (2017)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: · 2017
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., Aila, T.: · 2019
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Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: · 2019
Later among the works it cites.
Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., Simonyan, K.: · 2019
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Singan: Learning a generative model from a single natural image
Rott Shaham, T., Dekel, T., Michaeli, T.: · 2019
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High fidelity video prediction with large stochastic recurrent neural networks
Villegas, R., Pathak, A., Kannan, H., Erhan, D., Le, Q.V., Lee, H.: · 2019
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Sliced wasserstein generative models
Wu, J., Huang, Z., Acharya, D., Li, W., Thoma, J., Paudel, D.P., Gool, L.V.: · 2019
Later among the works it cites.
Efficient video generation on complex datasets
Clark, A., Donahue, J., Simonyan, K.: · 2019
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Patchvae: Learning local latent codes for recognition
Gupta, K., Singh, S., Shrivastava, A.: · 2020
Closest in time.
Improved techniques for training single-image gans (2020)
Hinz, T., Fisher, M., Wang, O., Wermter, S.: · 2020
Closest in time.