Flow-based propagation and spatiotemporal Transformer are two mainstream mechanisms
in video inpainting (VI). Despite the effectiveness of these components, they still suffer from …

Optical flow, which captures motion information across frames, is exploited in recent video
inpainting methods through propagating pixels along its trajectories. However, the hand …

Transformer, as a strong and flexible architecture for modelling long-range relations, has
been widely explored in vision tasks. However, when used in video inpainting that requires …

We introduce a new task--language-driven video inpainting which uses natural language
instructions to guide the inpainting process. This approach overcomes the limitations of …

Physical objects have inertia, which resists changes in the velocity and motion direction.
Inspired by this, we introduce inertia prior that optical flow, which reflects object motion in a …

Video inpainting remains a challenging problem to fill with plausible and coherent content in
unknown areas in video frames despite the prevalence of data-driven methods. Although …

Video inpainting aims to fill spatio-temporal" corrupted" regions with plausible content. To
achieve this goal, it is necessary to find correspondences from neighbouring frames to …

Video inpainting aims to fill the given spatiotemporal holes with realistic appearance but is
still a challenging task even with prosperous deep learning approaches. Recent works …

The key to video inpainting is to use correlation information from as many reference frames
as possible. Existing flow-based propagation methods split the video synthesis process into …

Image and video inpainting is a classic problem in computer vision and computer graphics,
aiming to fill in the plausible and realistic content in the missing areas of images and videos …