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Towards an End-to-End Framework for Flow-Guided Video Inpainting

CVPR 2022 · Video › Video segmentation and tracking · approach: Learned optical flow estimation · 145 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace FuseFormer with the end-to-end flow-guided frameworkVideo inpainting · YouTube-VOS testPSNR: 33.29 -> 33.71Helped
Replace FuseFormer with the end-to-end flow-guided frameworkVideo inpainting · DAVIS testVFID: 0.138 -> 0.116Helped
Remove motion supervision by removing flow consistency loss (ablation)E²FGVI flow completion · DAVISPSNR: 32.35 -> 32.08Hurt
Add flow completion to the pretrained flow estimates (ablation)E²FGVI flow completion · DAVISPSNR: 32.23 -> 32.35Helped
Add flow-guided feature propagation (ablation)E²FGVI · DAVISPSNR: 31.73 -> 32.15Helped
Add deformable convolution to flow-based propagation (ablation)E²FGVI feature propagation · DAVISPSNR: 32.15 -> 32.35Helped
Remove flow guidance from deformable-convolution propagation (ablation)E²FGVI feature propagation · DAVISPSNR: 32.35 -> 32.17Hurt
Replace focal attention with local window attention (ablation)E²FGVI attention mechanism · DAVISPSNR: 31.73 -> 31.57Hurt
Replace focal attention with vanilla global attention (ablation)E²FGVI attention mechanism · DAVISPSNR: 31.73 -> 31.74Helped
Replace online completion with offline FGVC-completed flows (ablation)E²FGVI flow completion · DAVISPSNR: 32.35 -> 32.38Helped
Replace online completion with offline FGVC-completed flows (ablation)E²FGVI flow completion · DAVISruntime (s/frame): 0.16 -> 1.21Hurt
Use the proposed hallucination module instead of an image inpainting model after flow pre-fill (ablation)DAVIS video inpainting · DAVISPSNR: 30.80 -> 31.74Helped

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