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The ability to choose an appropriate camera view among multiple cameras playsa vital role in TV shows delivery. But it is hard to figure out the statisticalpattern and apply intelligent processing due to the lack of high-qualitytraining data. To solve this issue, we first collect a novel benchmark on thissetting with four diverse scenarios including concerts, sports games, galashows, and contests, where each scenario contains 6 synchronized tracksrecorded by different cameras. It contains 88-hour raw videos that contributeto the 14-hour edited videos. Based on this benchmark, we further propose a newapproach temporal and contextual transformer that utilizes clues fromhistorical shots and other views to make shot transition decisions and predictwhich view to be used. Extensive experiments show that our method outperformsexisting methods on the proposed multi-camera editing benchmark.

Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows