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Inertia-Guided Flow Completion and Style Fusion for Video Inpainting

CVPR 2022 · Video › Video segmentation and tracking · approach: Learned optical flow estimation · 44 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use inertia-guided feature-domain flow alignment and aggregation (ablation)IGFC · DAVIS square masksEPE: 0.58 -> 0.56Helped
Use inertia-guided feature-domain flow alignment and aggregation (ablation)IGFC · DAVIS object masksPSNR: 34.90 -> 35.16Helped
Apply inertia warping in the feature domain rather than the flow domain (ablation)IGFC · DAVIS square masksPSNR: 32.91 -> 33.23Helped
Apply inertia warping in the feature domain rather than the flow domain (ablation)IGFC · DAVIS object masksEPE: 0.38 -> 0.35Helped
Add ternary census transform loss to flow completion training (ablation)IGFC · DAVISflow motion-boundary clarityHelped
Add ASFN to refine warped gradients using style statistics from valid regions (ablation)Ours · DAVIS square masksPSNR: 32.91 -> 33.23Helped
Add ASFN to refine warped gradients using style statistics from valid regions (ablation)Ours · DAVIS object masksPSNR: 34.88 -> 35.16Helped
Add ASFN to the DFGVI flow-guided framework (ablation)DFGVI · DAVIS square masksPSNR: 31.24 -> 31.22No clear effect
Add ASFN to the DFGVI flow-guided framework (ablation)DFGVI · DAVIS object masksPSNR: 33.12 -> 33.23Helped
Add ASFN to the FGVC flow-guided framework (ablation)FGVC · DAVIS square masksPSNR: 32.14 -> 32.37Helped
Add ASFN to the FGVC flow-guided framework (ablation)FGVC · DAVIS object masksPSNR: 33.91 -> 34.17Helped

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