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Recent learning-based inpainting algorithms have achieved compelling resultsfor completing missing regions after removing undesired objects in videos. Tomaintain the temporal consistency among the frames, 3D spatial and temporaloperations are often heavily used in the deep networks. However, these methodsusually suffer from memory constraints and can only handle low resolutionvideos. We propose STRA-Net, a novel spatial-temporal residual aggregationframework for high resolution video inpainting. The key idea is to first learnand apply a spatial and temporal inpainting network on the downsampled lowresolution videos. Then, we refine the low resolution results by aggregatingthe learned spatial and temporal image residuals (details) to the upsampledinpainted frames. Both the quantitative and qualitative evaluations show thatwe can produce more temporal-coherent and visually appealing results than thestate-of-the-art methods on inpainting high resolution videos.

Spatial-Temporal Residual Aggregation for High Resolution Video Inpainting