Papers › Video Enhancement with Task-Oriented Flow
Video Enhancement with Task-Oriented Flow
Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, William T. Freeman
Many video enhancement algorithms rely on optical flow to register frames in a video sequence. Precise flow estimation is however intractable; and optical flow itself is often a sub-optimal representation for particular video processing tasks. In this paper, we propose task-oriented flow (TOFlow), a motion representation learned in a self-supervised, task-specific manner. We design a neural network with a trainable motion estimation component and a video processing component, and train them jointly to learn the task-oriented flow. For evaluation, we build Vimeo-90K, a large-scale, high-quality video dataset for low-level video processing. TOFlow outperforms traditional optical flow on standard benchmarks as well as our Vimeo-90K dataset in three video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution.
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Code
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Tasks
Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Frame Interpolation | Middlebury | ToFlow | Interpolation Error | 5.49 | #7 of 11 | Archive leaderboard | report |
| Video Frame Interpolation | Vimeo90K | ToFlow | PSNR | 33.73 | #23 of 23 | Archive leaderboard | report |
| Video Super-Resolution | Vid4 - 4x upscaling - BD degradation | TOFlow | PSNR | 25.85 | #18 of 18 | Archive leaderboard | report |
| Video Super-Resolution | Vid4 - 4x upscaling - BD degradation | TOFlow | SSIM | 0.7659 | #18 of 18 | Archive leaderboard | report |
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