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Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e.g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).
Stochastic Latent Residual Video Prediction
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Recognizing human actions: a local SVM approach
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Dosovitskiy, A.; Fischer, P.; Ilg, E.; Hausser, P.; Hazirbas, C.; Golkov, V.; Van Der Smagt, P.; Cremers, D.; and Brox, T. 2015 · 2015
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
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Flownet 2.0: Evolution of optical flow estimation with deep networks
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Dual motion GAN for future-flow embedded video prediction
Liang, X.; Lee, L.; Dai, W.; and Xing, E. P. 2017 · 2017
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Video frame synthesis using deep voxel flow
Liu, Z.; Yeh, R. A.; Tang, X.; Liu, Y.; and Agarwala, A. 2017 · 2017
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Least squares generative adversarial networks
Mao, X.; Li, Q.; Xie, H.; Lau, R. Y.; Wang, Z.; and Paul Smolley, S. 2017 · 2017
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Wang, Y.; Long, M.; Wang, J.; Gao, Z.; and Philip, S. Y. 2017 · 2017
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Recurrent neural networks for multivariate time series with missing values
Che, Z.; Purushotham, S.; Cho, K.; Sontag, D.; and Liu, Y. 2018 · 2018
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Neural ordinary differential equations
Chen, T. Q.; Rubanova, Y.; Bettencourt, J.; and Duvenaud, D. K. 2018 · 2018
Depth-aware video frame interpolation
Bao, W.; Lai, W.-S.; Ma, C.; Zhang, X.; Gao, Z.; and Yang, M.-H. 2019 · 2019
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GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
De Brouwer, E.; Simm, J.; Arany, A.; and Moreau, Y. 2019 · 2019
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Augmented neural ODEs
Dupont, E.; Doucet, A.; and Teh, Y. W. 2019 · 2019
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Disentangling propagation and generation for video prediction
Gao, H.; Xu, H.; Cai, Q.-Z.; Wang, R.; Yu, F.; and Darrell, T. 2019 · 2019
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Learning to Focus and Track Extreme Climate Events
Kim, S.; Park, S.; Chung, S.; Lee, J.; Lee, Y.; Kim, H.; Prabhat, M.; and Choo, J. 2019 · 2019
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Predicting future frames using retrospective cycle GAN
Kwon, Y.-H.; and Park, M.-G. 2019 · 2019
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Cited alongside, same era.
Stochastic Video Generation with a Learned Prior
Denton, E.; and Fergus, R. 2018 · 2018
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Controllable video generation with sparse trajectories
Hao, Z.; Huang, X.; and Belongie, S. 2018 · 2018
Cited alongside, same era.
Super slomo: High quality estimation of multiple intermediate frames for video interpolation
Jiang, H.; Sun, D.; Jampani, V.; Yang, M.-H.; Learned-Miller, E.; and Kautz, J. 2018 · 2018
Cited alongside, same era.
Stochastic adversarial video prediction
Lee, A. X.; Zhang, R.; Ebert, F.; Abbeel, P.; Finn, C.; and Levine, S. 2018 · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018 · 2018
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Eidetic 3d LSTM: A model for video prediction and beyond
Wang, Y.; Jiang, L.; Yang, M.-H.; Li, L.-J.; Long, M.; and Fei-Fei, L. 2019a
Cited in the paper.
Unsupervised Video Interpolation Using Cycle Consistency
Reda, F. A.; Sun, D.; Dundar, A.; Shoeybi, M.; Liu, G.; Shih, K. J.; Tao, A.; Kautz, J.; and Catanzaro, B. 2019 · 2019
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Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Rubanova, Y.; Chen, T. Q.; and Duvenaud, D. K. 2019 · 2019
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Animating arbitrary objects via deep motion transfer
Siarohin, A.; Lathuilière, S.; Tulyakov, S.; Ricci, E.; and Sebe, N. 2019 · 2019
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ODE 2 VAE: Deep generative second order ODEs with Bayesian neural networks
Yildiz, C.; Heinonen, M.; and Lahdesmaki, H. 2019 · 2019
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