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I am coming from a background in deep learning techniques for computer vision like image classification, action recognition from skeletal data, and semantic segmentation. I have read several papers on Optical Flow, but I'm wondering what the nature of the annotation is for optical flow.

Specifically, supposing we have two images I_1, and I_2, both of the same resolution. For a dense optical flow prediction task, there must be some ground truth image G of the same resolution with annotations at each pixel.

My question is, what are the values of G?

For instance, suppose a pixel in image I_1 at (x_1, y_1) "moves" to pixel (x_2, y_2) in I_2, I can imagine the ground truth being a tuple of values (g_x, g_y). These could be the normalized l_1 distances computed coordinate-wise but I'm not sure if this is what's done in practice, or is it a single regression value per pixel?

I'm assuming pixels that have no movement are given a value of 0. There may even be some "don't train/don't train" pixels, perhaps given a sentinel value of -1.

Any insights on how the labeling of optical flow works much appreciated.

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See for examples the Middlebury datasets: https://vision.middlebury.edu/flow/data/

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