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3 months ago

Flow-edge Guided Video Completion

Chen Gao Ayush Saraf Jia-Bin Huang Johannes Kopf

Flow-edge Guided Video Completion

Abstract

We present a new flow-based video completion algorithm. Previous flow completion methods are often unable to retain the sharpness of motion boundaries. Our method first extracts and completes motion edges, and then uses them to guide piecewise-smooth flow completion with sharp edges. Existing methods propagate colors among local flow connections between adjacent frames. However, not all missing regions in a video can be reached in this way because the motion boundaries form impenetrable barriers. Our method alleviates this problem by introducing non-local flow connections to temporally distant frames, enabling propagating video content over motion boundaries. We validate our approach on the DAVIS dataset. Both visual and quantitative results show that our method compares favorably against the state-of-the-art algorithms.

Code Repositories

vt-vl-lab/FGVC
Official
pytorch
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
video-inpainting-on-davisFGVC
Ewarp: 0.1586
PSNR: 30.80
SSIM: 0.9497
VFID: 0.165
video-inpainting-on-hqvi-240pFGVC
LPIPS: 0.0409
PSNR: 28.37
SSIM: 0.9383
VFID: 0.2436
video-inpainting-on-hqvi-480pFGVC
LPIPS: 0.0388
PSNR: 28.63
SSIM: 0.9433
VFID: 0.0470
video-inpainting-on-youtube-vosFGVC
Ewarp: 0.1022
PSNR: 29.67
SSIM: 0.9403
VFID: 0.064

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Flow-edge Guided Video Completion | Papers | HyperAI