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Given two consecutive frames, video interpolation aims at generating intermediate frame(s) to form both spatially and temporally coherent video sequences.
Determining optical flow
B. Horn and B. Schunck · 1981
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Performance of optical flow techniques
J. Barron, D. Fleet, and S. Beauchemin · 1994
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Prediction error as a quality metric for motion and stereo
R. Szeliski · 1999
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On the spatial statistics of optical flow
S. Roth and M. J. Black · 2007
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Learning for optical flow using stochastic optimization
Y. Li and D. P. Huttenlocher · 2008
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Learning optical flow
D. Sun, S. Roth, J. P. Lewis, and M. J. Black · 2008
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Occlusion reasoning for temporal interpolation using optical flow
E. Herbst, S. Seitz, and S. Baker · 2009
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Moving gradients: a path-based method for plausible image interpolation
D. Mahajan, F.-C. Huang, W. Matusik, R. Ramamoorthi, and P. Belhumeur · 2009
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A perceptually motivated online benchmark for image matting
C. Rhemann, C. Rother, J. Wang, M. Gelautz, P. Kohli, and P. Rott · 2009
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A database and evaluation methodology for optical flow
S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, and R. Szeliski · 2011
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Large displacement optical flow: Descriptor matching in variational motion estimation
T. Brox and J. Malik · 2011
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A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
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Are we ready for autonomous driving? The KITTI vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Ucf101: A dataset of 101 human action classes from videos in the wild
K. Soomro, A. R. Zamir, and M. Shah · 2012
Cited alongside, same era.
Motion detail preserving optical flow estimation
L. Xu, J. Jia, and Y. Matsushita · 2012
Cited alongside, same era.
Deepflow: Large displacement optical flow with deep matching
P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Flownet: Learning optical flow with convolutional networks
Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness
J. J. Yu, A. W. Harley, and K. G. Derpanis · 2016
Later among the works it cites.
View synthesis by appearance flow
T. Zhou, S. Tulsiani, W. Sun, J. Malik, and A. A. Efros · 2016
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Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
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Slow flow: Exploiting high-speed cameras for accurate and diverse optical flow reference data
J. Janai, F. Güney, J. Wulff, M. Black, and A. Geiger · 2017
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Dual motion GAN for future-flow embedded video prediction
X. Liang, L. Lee, W. Dai, and E. P. Xing · 2017
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Video frame synthesis using deep voxel flow
Z. Liu, R. Yeh, X. Tang, Y. Liu, and A. Agarwala · 2017
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A. Dosovitskiy, P. Fischery, E. Ilg, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, T. Brox, et al · 2015
Cited alongside, same era.
Phase-based frame interpolation for video
S. Meyer, O. Wang, H. Zimmer, M. Grosse, and A. Sorkine-Hornung · 2015
Cited alongside, same era.
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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High accuracy correspondence field estimation via mst based patch matching
F. Zhang, S. Xu, and X. Zhang · 2015
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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Learning image matching by simply watching video
G. Long, L. Kneip, J. M. Alvarez, H. Li, X. Zhang, and Q. Yu · 2016
Cited alongside, same era.
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Video frame interpolation via adaptive convolution
S. Niklaus, L. Mai, and F. Liu · 2017
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Video frame interpolation via adaptive separable convolution
S. Niklaus, L. Mai, and F. Liu · 2017
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Optical flow estimation using a spatial pyramid network
A. Ranjan and M. J. Black · 2017
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Deep video deblurring
S. Su, M. Delbracio, J. Wang, G. Sapiro, W. Heidrich, and O. Wang · 2017
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Optical flow in mostly rigid scenes
J. Wulff, L. Sevilla-Lara, and M. J. Black · 2017
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Accurate optical flow via direct cost volume processing
J. Xu, R. Ranftl, and V. Koltun · 2017
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz · 2018
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