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.
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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.
How to efficiently utilize temporal information to recover videos in a consistent way is the main issue for video inpainting problems.
Free-form video inpainting is a very challenging task that could be widely used for video editing such as text removal.
In this paper, we propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting.