Long Video Diffusion Generation with Segmented Cross-Attention and
  Content-Rich Video Data Curation
Authors: name: Xin Yan, name: Yuxuan Cai, name: Qiuyue Wang, name: Yuan Zhou, name: Wenhao Huang, name: Huan Yang

We introduce Presto, a novel video diffusion model designed to generate
15-second videos with long-range coherence and rich content. Extending video
generation methods to maintain scenario diversity over long durations presents
significant challenges. To address this, we propose a Segmented Cross-Attention
(SCA) strategy, which splits hidden states into segments along the temporal
dimension, allowing each segment to cross-attend to a corresponding
sub-caption. SCA requires no additional parameters, enabling seamless
incorporation into current DiT-based architectures. To facilitate high-quality
long video generation, we build the LongTake-HD dataset, consisting of 261k
content-rich videos with scenario coherence, annotated with an overall video
caption and five progressive sub-captions. Experiments show that our Presto
achieves 78.5% on the VBench Semantic Score and 100% on the Dynamic Degree,
outperforming existing state-of-the-art video generation methods. This
demonstrates that our proposed Presto significantly enhances content richness,
maintains long-range coherence, and captures intricate textual details. More
details are displayed on our project page: https://presto-video.github.io/.