Recently, removing objects from videos and filling in the erased regions
using deep video inpainting (VI) algorithms has attracted considerable
attention. Usually, a video sequence and object segmentation masks for all
frames are required as the input for this task. However, in real-world
applications, providing segmentation masks for all frames is quite difficult
and inefficient. Therefore, we deal with VI in a one-shot manner, which only
takes the initial frame's object mask as its input. Although we can achieve
that using naive combinations of video object segmentation (VOS) and VI
methods, they are sub-optimal and generally cause critical errors. To address
that, we propose a unified pipeline for one-shot video inpainting (OSVI). By
jointly learning mask prediction and video completion in an end-to-end manner,
the results can be optimal for the entire task instead of each separate module.
Additionally, unlike the two stage methods that use the predicted masks as
ground truth cues, our method is more reliable because the predicted masks can
be used as the network's internal guidance. On the synthesized datasets for
OSVI, our proposed method outperforms all others both quantitatively and
qualitatively.

本研究提出了一种统一的方法来实现一次性视频修复，它可以同时进行遮罩预测和视频完成，并通过端到端的学习来优化整个任务，其效果在合成数据集上表现最佳。