Blind video decaptioning is a problem of automatically removing text overlays and inpainting the occluded parts in videos without any input masks.
Free-form video inpainting is a very challenging task that could be widely used for video editing such as text removal.
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.
We propose a novel DNN-based framework called the Copy-and-Paste Networks for video inpainting that takes advantage of additional information in other frames of the video.
Video object removal is a challenging task in video processing that often requires massive human efforts.
We propose a new tensor completion method based on tensor trains.
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.