  Video inpainting aims to restore missing regions of a video and has many
applications such as video editing and object removal. However, existing
methods either suffer from inaccurate short-term context aggregation or rarely
explore long-term frame information. In this work, we present a novel context
aggregation network to effectively exploit both short-term and long-term frame
information for video inpainting. In the encoding stage, we propose
boundary-aware short-term context aggregation, which aligns and aggregates,
from neighbor frames, local regions that are closely related to the boundary
context of missing regions into the target frame. Furthermore, we propose
dynamic long-term context aggregation to globally refine the feature map
generated in the encoding stage using long-term frame features, which are
dynamically updated throughout the inpainting process. Experiments show that it
outperforms state-of-the-art methods with better inpainting results and fast
inpainting speed.
