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Found 4 papers, 2 papers with code
Unsupervised Video Interpolation by Learning Multilayered 2.5D Motion Fields
This implicit neural representation learns the video as a space-time continuum, allowing frame interpolation at any temporal resolution.
Learning to Estimate Hidden Motions with Global Motion Aggregation
We demonstrate that the optical flow estimates in the occluded regions can be significantly improved without damaging the performance in non-occluded regions.
Ranked #5 on
Optical Flow Estimation
on Sintel-final
Learning Optical Flow from a Few Matches
In this paper, we show that the dense correlation volume representation is redundant and accurate flow estimation can be achieved with only a fraction of elements in it.
Ranked #11 on
Optical Flow Estimation
on KITTI 2015 (train)
Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization
We address the problem of joint optical flow and camera motion estimation in rigid scenes by incorporating geometric constraints into an unsupervised deep learning framework.
