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A Unified Pyramid Recurrent Network for Video Frame Interpolation

CVPR 2023 · Video › Video segmentation and tracking · approach: Learned optical flow estimation · 69 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add an upsampled intermediate-frame estimate for iterative synthesis (ablation)UPR-Net · SNU-FILM hardPSNR: 30.57 -> 30.67Helped
Add an upsampled intermediate-frame estimate for iterative synthesis (ablation)UPR-Net · 4K1000FPS X-TESTPSNR: 28.91 -> 30.13Helped
Add an upsampled intermediate-frame estimate for iterative synthesis (ablation)UPR-Net · Vimeo90KPSNR: 36.02 -> 36.03No clear effect
Keep both flow and synthesis modules recurrent (ablation)UPR-Net · 4K1000FPS X-TESTPSNR: 24.01 -> 30.13Helped
Keep both flow and synthesis modules recurrent (ablation)UPR-Net · SNU-FILM hardPSNR: 30.25 -> 30.67Helped
Unify flow estimation and frame synthesis in one pipeline (ablation)UPR-Net · 4K1000FPS X-TESTPSNR: 29.89 -> 30.13Helped
Unify flow estimation and frame synthesis in one pipeline (ablation)UPR-Net · Vimeo90KPSNR: 35.96 -> 36.03Helped
Add a correlation volume to the flow module (ablation)UPR-Net · Vimeo90KPSNR: 35.81 -> 36.03Helped
Add context features to frame synthesis (ablation)UPR-Net · Vimeo90KPSNR: 35.63 -> 36.03Helped
Add context features to frame synthesis (ablation)UPR-Net · SNU-FILM hardPSNR: 30.68 -> 30.67No clear effect
Skip high-resolution levels for flow and synthesis on 4K input (ablation)UPR-Net large · 4K1000FPS X-TESTPSNR: 30.14 -> 30.68Helped

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