Movie trailers are an essential tool for promoting films and attracting
audiences. However, the process of creating trailers can be time-consuming and
expensive. To streamline this process, we propose an automatic trailer
generation framework that generates plausible trailers from a full movie by
automating shot selection and composition. Our approach draws inspiration from
machine translation techniques and models the movies and trailers as sequences
of shots, thus formulating the trailer generation problem as a
sequence-to-sequence task. We introduce Trailer Generation Transformer (TGT), a
deep-learning framework utilizing an encoder-decoder architecture. TGT movie
encoder is tasked with contextualizing each movie shot representation via
self-attention, while the autoregressive trailer decoder predicts the feature
representation of the next trailer shot, accounting for the relevance of shots'
temporal order in trailers. Our TGT significantly outperforms previous methods
on a comprehensive suite of metrics.

我们提出了一种自动预告片生成框架，该框架通过自动化镜头选择和组合从完整电影中生成可信的预告片。我们的方法受到机器翻译技术的启发，将电影和预告片建模为镜头序列，从而将预告片生成问题形式化为序列到序列的任务。我们引入了 Trailer Generation Transformer（TGT），这是一个利用编码器 - 解码器架构的深度学习框架。TGT 电影编码器通过自注意力上下文化每个电影镜头表示，而自回归预告片解码器预测下一个预告片镜头的特征表示，考虑镜头在预告片中的时间顺序的相关性。我们的 TGT 在全面的度量套件上明显优于先前的方法。

自动化电影预告片生成

Towards Automated Movie Trailer Generation

Shots are key narrative elements of various videos, e.g. movies, TV series,
and user-generated videos that are thriving over the Internet. The types of
shots greatly influence how the underlying ideas, emotions, and messages are
expressed. The technique to analyze shot types is important to the
understanding of videos, which has seen increasing demand in real-world
applications in this era. Classifying shot type is challenging due to the
additional information required beyond the video content, such as the spatial
composition of a frame and camera movement. To address these issues, we propose
a learning framework Subject Guidance Network (SGNet) for shot type
recognition. SGNet separates the subject and background of a shot into two
streams, serving as separate guidance maps for scale and movement type
classification respectively. To facilitate shot type analysis and model
evaluations, we build a large-scale dataset MovieShots, which contains 46K
shots from 7K movie trailers with annotations of their scale and movement
types. Experiments show that our framework is able to recognize these two
attributes of shot accurately, outperforming all the previous methods.

通过分离主体和背景，并分别对比它们的规模和运动类型，我们提出了 Subject Guidance Network（SGNet）的学习框架，以识别视频镜头的类型。我们构建了一个庞大的数据集 MovieShots，其中包含 7K 个电影预告片的 46K 个视角，用于促进镜头类型的分析和模型评估。实验证明，我们的方法能够准确地识别镜头的规模和运动类型，并胜过所有先前的方法。