SEINE: Short-to-Long Video Diffusion Model for Generative Transition and
  Prediction
Authors: name: Xinyuan Chen, name: Yaohui Wang, name: Lingjun Zhang, name: Shaobin Zhuang, name: Xin Ma, name: Jiashuo Yu, name: Yali Wang, name: Dahua Lin, name: Yu Qiao, name: Ziwei Liu

Recently video generation has achieved substantial progress with realistic
results. Nevertheless, existing AI-generated videos are usually very short
clips ("shot-level") depicting a single scene. To deliver a coherent long video
("story-level"), it is desirable to have creative transition and prediction
effects across different clips. This paper presents a short-to-long video
diffusion model, SEINE, that focuses on generative transition and prediction.
The goal is to generate high-quality long videos with smooth and creative
transitions between scenes and varying lengths of shot-level videos.
Specifically, we propose a random-mask video diffusion model to automatically
generate transitions based on textual descriptions. By providing the images of
different scenes as inputs, combined with text-based control, our model
generates transition videos that ensure coherence and visual quality.
Furthermore, the model can be readily extended to various tasks such as
image-to-video animation and autoregressive video prediction. To conduct a
comprehensive evaluation of this new generative task, we propose three
assessing criteria for smooth and creative transition: temporal consistency,
semantic similarity, and video-text semantic alignment. Extensive experiments
validate the effectiveness of our approach over existing methods for generative
transition and prediction, enabling the creation of story-level long videos.
Project page: https://vchitect.github.io/SEINE-project/ .