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Optical flow is inherently a 2D search problem, and thus the computationalcomplexity grows quadratically with respect to the search window, making largedisplacements matching infeasible for high-resolution images. In this paper, wetake inspiration from Transformers and propose a new method for high-resolutionoptical flow estimation with significantly less computation. Specifically, a 1Dattention operation is first applied in the vertical direction of the targetimage, and then a simple 1D correlation in the horizontal direction of theattended image is able to achieve 2D correspondence modeling effect. Thedirections of attention and correlation can also be exchanged, resulting in two3D cost volumes that are concatenated for optical flow estimation. The novel 1Dformulation empowers our method to scale to very high-resolution input imageswhile maintaining competitive performance. Extensive experiments on Sintel,KITTI and real-world 4K ($2160 \times 3840$) resolution images demonstrated theeffectiveness and superiority of our proposed method. Code and models areavailable at \url{https://github.com/haofeixu/flow1d}.

High-Resolution Optical Flow from 1D Attention and Correlation