Vript
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🎬 Vript: Refine Video Captioning into Video Scripting We construct a fine-grained video-text dataset with 12K annotated high-resolution videos (~400k clips). The annotation of this dataset is inspired by the video script. If we want to make a video, we have to first write a script to organize how to shoot the scenes in the videos. To shoot a scene, we need to decide the content, shot type (medium shot, close-up, etc), and how the camera moves (panning, tilting, etc). Therefore, we extend video captioning to video scripting by annotating the videos in the format of video scripts. Different from the previous video-text datasets, we densely annotate the entire videos without discarding any scenes and each scene has a caption with ~145 words. Besides the vision modality, we transcribe the voice-over into text and put it along with the video title to give more background information for annotating the videos.
Vript is a fine-grained video-text dataset comprising high-resolution videos (over 400k clips) paired with 12,000 annotations. The annotations of this dataset are inspired by video scripts. To produce a video, one must first draft a script to outline the shooting plan for each scene in the video. For shooting a single scene, decisions must be made regarding its content, shot type (e.g., medium shot, close-up) and camera movements (e.g., pan, tilt). Thus, drawing inspiration from the structure of video scripts, we annotate videos following the standard video script format. Unlike prior video-text datasets, we conduct dense annotation across the entire video without discarding any scene, with each scene accompanied by a descriptive caption of approximately 145 words. In addition to the visual modality, we also transcribe voiceovers into text and align them with the video captions to provide additional contextual information for the video annotations.
