  The ability to choose an appropriate camera view among multiple cameras plays
a vital role in TV shows delivery. But it is hard to figure out the statistical
pattern and apply intelligent processing due to the lack of high-quality
training data. To solve this issue, we first collect a novel benchmark on this
setting with four diverse scenarios including concerts, sports games, gala
shows, and contests, where each scenario contains 6 synchronized tracks
recorded by different cameras. It contains 88-hour raw videos that contribute
to the 14-hour edited videos. Based on this benchmark, we further propose a new
approach temporal and contextual transformer that utilizes clues from
historical shots and other views to make shot transition decisions and predict
which view to be used. Extensive experiments show that our method outperforms
existing methods on the proposed multi-camera editing benchmark.
