We tackle the long video generation problem, i.e.~generating videos beyond
the output length of video generation models. Due to the computation resource
constraints, video generation models can only generate video clips that are
relatively short compared with the length of real videos. Existing works apply
a sliding window approach to generate long videos at inference time, which is
often limited to generating recurrent events or homogeneous content. To
generate long videos covering diverse content and multiple events, we propose
to use additional guidance to control the video generation process. We further
present a two-stage approach to the problem, which allows us to utilize
existing video generation models to generate high-quality videos within a small
time window while modeling the video holistically based on the input guidance.
The proposed approach is complementary to existing efforts on video generation,
which focus on generating realistic video within a fixed time window. Extensive
experiments on challenging real-world videos validate the benefit of the
proposed method, which improves over state-of-the-art by up to 9.5% in
objective metrics and is preferred by users more than 80% of time.

提出使用指导信息和两阶段方法来生成多样化的长视频，该方法在客观指标上比现有技术提高多达 9.5％，得到超过 80％的用户偏好。

超越单一视频片段的视频生成

Video Generation Beyond a Single Clip

We propose a new approach to generate multiple variants of the target summary
with diverse content and varying lengths, then score and select admissible ones
according to users' needs. Abstractive summarizers trained on single reference
summaries may struggle to produce outputs that achieve multiple desirable
properties, i.e., capturing the most important information, being faithful to
the original, grammatical and fluent. In this paper, we propose a two-staged
strategy to generate a diverse set of candidate summaries from the source text
in stage one, then score and select admissible ones in stage two. Importantly,
our generator gives a precise control over the length of the summary, which is
especially well-suited when space is limited. Our selectors are designed to
predict the optimal summary length and put special emphasis on faithfulness to
the original text. Both stages can be effectively trained, optimized and
evaluated. Our experiments on benchmark summarization datasets suggest that
this paradigm can achieve state-of-the-art performance.

本研究提出一种新方法，通过生成多个变体的目标摘要，这些摘要具有不同的内容和不同的长度，并根据用户的需要进行评分和选择，该方法可用于控制摘要长度，从而实现最佳的信息概括，保证真实性并确保流畅。