23 papers with code • 2 benchmarks • 6 datasets
The goal of Video Inpainting is to fill in missing regions of a given video sequence with contents that are both spatially and temporally coherent. Video Inpainting, also known as video completion, has many real-world applications such as undesired object removal and video restoration.
Free-form video inpainting is a very challenging task that could be widely used for video editing such as text removal.
How to efficiently utilize temporal information to recover videos in a consistent way is the main issue for video inpainting problems.
To get clear street-view and photo-realistic simulation in autonomous driving, we present an automatic video inpainting algorithm that can remove traffic agents from videos and synthesize missing regions with the guidance of depth/point cloud.
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
We propose the first deep learning solution to video frame inpainting, a challenging instance of the general video inpainting problem with applications in video editing, manipulation, and forensics.
Blind video decaptioning is a problem of automatically removing text overlays and inpainting the occluded parts in videos without any input masks.