This edition features ProPainter, a video editing tool developed by Nanyang Technological University. Its features include watermark removal, object removal, video cropping, and video completion.
ProPainter is very easy to use. Users simply need to import the video they want to edit into the tool and click on the corresponding function button to remove moving objects or watermarks with just one click. The tool will automatically analyze each frame of the video and accurately identify and remove objects based on their motion trajectory or the position of the watermark.
The object removal feature of this tool helps users easily remove moving objects such as people and vehicles from videos, making the footage cleaner and more focused. Additionally, ProPainter can also remove video watermarks with just one click. Whether it's for copyright protection or other reasons, users can select the corresponding function and the tool will automatically detect and remove watermarks from the video, preserving its originality.
ProPainter's simple operation and efficient recognition capabilities make video editing more convenient and efficient. Whether you are an individual user or a professional video editor, you can benefit greatly from ProPainter.
ProPainter features include:
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Object removal: Remove objects from videos.
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Watermark removal: Remove watermarks from videos.
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Video completion: Complete masked videos.
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Video cropping: Expand the view of videos.
ProPainter consists of three key components: cyclic flow completion, dual-domain propagation, and mask-guided sparse transformer. First, we use an efficient cyclic flow completion network to complete the corrupted flow field. Then, we perform propagation in both the jointly trained image domain and feature domain. This approach allows us to explore the correspondence between global and local temporal frameworks, resulting in more reliable and effective propagation. The subsequent mask-guided sparse transformer block refines the propagated features using spatiotemporal attention and a sparse strategy that only considers a subset of tokens. This improves efficiency, reduces memory consumption, and maintains performance.
The code and models for the tool are now publicly available.
Interested friends can try it out themselves.