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 this https URL.

本文提出了一种用于盲视频时间一致性的方法，该方法旨在解决仅在每个视频帧上独立应用图像处理算法导致的时间不一致问题。我们展示了通过在 Deep Video Prior（DVP）视频上训练卷积神经网络实现时间一致性的方法，并针对挑战性的多模态不一致性问题提出了一种经过精心设计的迭代加权训练策略。我们通过 7 个计算机视觉任务的广泛定量和感知实验展示了我们的方法的有效性，并证明了我们的方法在盲视频时间一致性方面优于现有技术水平。