  Recent years have seen remarkable advances in visual understanding. However,
how to understand a story-based long video with artistic styles, e.g. movie,
remains challenging. In this paper, we introduce MovieNet -- a holistic dataset
for movie understanding. MovieNet contains 1,100 movies with a large amount of
multi-modal data, e.g. trailers, photos, plot descriptions, etc. Besides,
different aspects of manual annotations are provided in MovieNet, including
1.1M characters with bounding boxes and identities, 42K scene boundaries, 2.5K
aligned description sentences, 65K tags of place and action, and 92K tags of
cinematic style. To the best of our knowledge, MovieNet is the largest dataset
with richest annotations for comprehensive movie understanding. Based on
MovieNet, we set up several benchmarks for movie understanding from different
angles. Extensive experiments are executed on these benchmarks to show the
immeasurable value of MovieNet and the gap of current approaches towards
comprehensive movie understanding. We believe that such a holistic dataset
would promote the researches on story-based long video understanding and
beyond. MovieNet will be published in compliance with regulations at
https://movienet.github.io.
