SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction

Xinyuan Chen1*, Yaohui Wang1*, Lingjun Zhang2,1‡, Shaobin Zhuang1,3, Xin Ma4,1‡, Jiashuo Yu1, Yali Wang1, Dahua Lin1†, Yu Qiao1†, Ziwei Liu6†
1Shanghai Artificial Intelligence Laboratory  2East China Normal University  3Shanghai Jiao Tong University  4Monash University   5Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences   6S-Lab, Nanyang Technological University
*Equal contribution †Corresponding authors
‡Work done during internship at Shanghai AI Laboratory
Paper arXiv Code

Image-to-Video Generation

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Input image

Cars on the snowy ground of Doomsday Highway.

Input Image

Frozen City with crowded cars on the snowy ground.

Input Image

Space elevator.
Notes：Our model excels with a width of 512 pixels. For results of larger sizes, a super-resolution algorithm is employed.

Transition Results

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Scene 1

Scene 2

Spiderman becomes a sand sculpture.

Scene 1

Scene 2

Flying through the clouds, a landscape appears.

Scene 1

Scene 2

A cat from sitting on the coach transfers to lying on the sand.

Diverse Results for Transition

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Example 1

Reference scenes



Example 2

Reference scenes

Auto-regressive Video Prediction Results

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Long Video Demo

The red boxes represent the transitions generated by our model, while the blue boxes (in the end of video) represent the long-shot videos generated through prediction.

Notes：Our model excels with a width of 512 pixels. For results of larger sizes, a super-resolution algorithm is employed.

Abstract

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.