We propose a novel video inpainting algorithm that simultaneously
hallucinates missing appearance and motion (optical flow) information, building
upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network
architectures to enforce plausible texture in static images. In extending DIP
to video we make two important contributions. First, we show that coherent
video inpainting is possible without a priori training. We take a generative
approach to inpainting based on internal (within-video) learning without
reliance upon an external corpus of visual data to train a one-size-fits-all
model for the large space of general videos. Second, we show that such a
framework can jointly generate both appearance and flow, whilst exploiting
these complementary modalities to ensure mutual consistency. We show that
leveraging appearance statistics specific to each video achieves visually
plausible results whilst handling the challenging problem of long-term
consistency.

提出了一种新颖的视频修复算法，通过 Deep Image Prior（DIP）推出的卷积网络，同时生成缺失的外观和光流信息，并采用一种无需先验训练的生成式方法进行修复。该方法具有良好的视觉效果和长期一致性。