Papers › DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework
DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework
Henrique Morimitsu, Xiaobin Zhu, Roberto M. Cesar Jr., Xiangyang Ji, Xu-Cheng Yin
Optical flow estimation is essential for video processing tasks, such as restoration and action recognition. The quality of videos is constantly increasing, with current standards reaching 8K resolution. However, optical flow methods are usually designed for low resolution and do not generalize to large inputs due to their rigid architectures. They adopt downscaling or input tiling to reduce the input size, causing a loss of details and global information. There is also a lack of optical flow benchmarks to judge the actual performance of existing methods on high-resolution samples. Previous works only conducted qualitative high-resolution evaluations on hand-picked samples. This paper fills this gap in optical flow estimation in two ways. We propose DPFlow, an adaptive optical flow architecture capable of generalizing up to 8K resolution inputs while trained with only low-resolution samples. We also introduce Kubric-NK, a new benchmark for evaluating optical flow methods with input resolutions ranging from 1K to 8K. Our high-resolution evaluation pushes the boundaries of existing methods and reveals new insights about their generalization capabilities. Extensive experimental results show that DPFlow achieves state-of-the-art results on the MPI-Sintel, KITTI 2015, Spring, and other high-resolution benchmarks.
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Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Optical Flow Estimation | KITTI 2015 | DPFlow | Fl-all | 3.56 | #3 of 18 | Archive leaderboard | report |
| Optical Flow Estimation | KITTI 2015 | DPFlow | Fl-fg | 4.93 | #3 of 18 | Archive leaderboard | report |
| Optical Flow Estimation | KITTI 2015 (train) | DPFlow | EPE | 3.37 | #2 of 19 | Archive leaderboard | report |
| Optical Flow Estimation | KITTI 2015 (train) | DPFlow | F1-all | 11.1 | #2 of 19 | Archive leaderboard | report |
| Optical Flow Estimation | Sintel-clean | DPFlow | Average End-Point Error | 1.046 | #3 of 29 | Archive leaderboard | report |
| Optical Flow Estimation | Sintel-final | DPFlow | Average End-Point Error | 1.975 | #2 of 28 | Archive leaderboard | report |
| Optical Flow Estimation | Spring | DPFlow | 1px total | 3.442 | #2 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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