  Applying an image processing algorithm independently to each video frame
often leads to temporal inconsistency in the resulting video. To address this
issue, we present a novel and general approach for blind video temporal
consistency. Our method is only trained on a pair of original and processed
videos directly instead of a large dataset. Unlike most previous methods that
enforce temporal consistency with optical flow, we show that temporal
consistency can be achieved by training a convolutional neural network on a
video with Deep Video Prior (DVP). Moreover, a carefully designed iteratively
reweighted training strategy is proposed to address the challenging multimodal
inconsistency problem. We demonstrate the effectiveness of our approach on 7
computer vision tasks on videos. Extensive quantitative and perceptual
experiments show that our approach obtains superior performance than
state-of-the-art methods on blind video temporal consistency. We further extend
DVP to video propagation and demonstrate its effectiveness in propagating three
different types of information (color, artistic style, and object
segmentation). A progressive propagation strategy with pseudo labels is also
proposed to enhance DVP's performance on video propagation. Our source codes
are publicly available at https://github.com/ChenyangLEI/deep-video-prior.
