Optical Flow Estimation is the problem of finding pixel-wise motions between consecutive images.
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We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.
The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
Ranked #3 on Video Frame Interpolation on UCF101
Finally, the two input images are warped and linearly fused to form each intermediate frame.
Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.
Optical flow estimation has not been among the tasks where CNNs were successful.
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.
We investigate two crucial and closely related aspects of CNNs for optical flow estimation: models and training.
Ranked #2 on Optical Flow Estimation on KITTI 2012
It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow.
Ranked #2 on Dense Pixel Correspondence Estimation on HPatches