SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction (2310.20700v2)

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. Project page: https://vchitect.github.io/SEINE-project/ .

• The paper presents the SEINE model that generates seamless long video transitions using a random-mask diffusion approach combined with text-based guidance.
• It achieves superior temporal consistency and semantic alignment compared to traditional morphing and interpolation techniques.
• The model extends its applicability to video production, digital storytelling, and automated editing by enhancing narrative-driven video generation.

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

The paper "SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction" presents an innovative framework aimed at tackling the challenges associated with generating long videos that maintain coherent visual storytelling. Specifically, it makes substantial contributions toward creating smooth and creative transitions between different scenes, a capability crucial for producing story-level long videos in cinematic and industrial applications.

Problem Addressed

Despite advancements in video generation using diffusion and other AI models, the resulting videos are often limited to short clips depicting a single scene, falling short when it comes to generating coherent long videos with scene transitions. Traditional methods like morphing and predefined algorithmic transitions (e.g., dissolves, fades, wipes) lack the flexibility and creative scope required for complex storytelling. The primary focus of this paper is to introduce a model that can perform generative transitions, filling the gap between short, disjoint video clips, and thereby supporting the production of comprehensive long videos.

Methodology

The cornerstone of this work is the SEINE model, which extends the capabilities of short video generation methods to produce long videos with sophisticated transitions. The SEINE framework hinges on a random-mask video diffusion model that integrates textual descriptions to guide the generative process:

• Random-Mask Diffusion Model: The model utilizes a random-mask mechanism to handle the generation of intermediate frames between two given scene images. This approach ensures temporal consistency and semantic coherence throughout the sequence.
• Text-Based Control: By leveraging textual descriptions, SEINE is capable of generating transitions that respect the provided narrative, enhancing the alignment between the visual and textual context.

Evaluation Criteria

To objectively assess the quality of the generated transitions, the authors propose three metrics:

1. Temporal Consistency: The generated frames must change smoothly over time.
2. Semantic Similarity: The intermediate frames should resemble the given scene images while evolving naturally.
3. Video-Text Semantic Alignment: The generated frames should align well with the provided textual descriptions.

These criteria ensure that the transitions not only look visually seamless but also make narrative sense.

Experimental Results

The evaluations performed demonstrate the superiority of the SEINE model in generating smooth transitions that maintain high visual quality and semantic coherence. Here are some key numerical results:

• Comparative evaluations using CLIPSIM scores show that SEINE outperforms traditional techniques like morphing and interpolation-based methods.
• Human preference studies indicate a higher satisfaction rate for SEINE-generated transitions over those created by existing methods.

Applications

The SEINE model's ability to generate high-quality, long videos with coherent transitions makes it a valuable tool for several applications:

• Video and Film Production: SEINE can be integrated into video editing software to automate and enhance the transition between scenes, significantly saving post-production time and effort.
• Content Creation: Animators and content creators can leverage SEINE to produce intricate animations and dynamic video content from static images, expanding the creative possibilities.
• Storytelling in Digital Media: By enabling coherent long video generation, SEINE can enhance narrative techniques in digital storytelling, game design, and interactive media platforms.

Implications and Future Work

The implications of SEINE extend beyond merely improving video transition quality. It paves the way for automation in video production, making complex video editing more accessible and reducing the need for labor-intensive manual editing. From a theoretical perspective, this work advances our understanding of diffusion models and their application in video generation tasks.

Future developments could include refining the alignment between textual descriptions and generated video content to ensure even more precise control over narrative-driven video generation. In addition, integrating SEINE with real-world datasets devoid of watermarks and biases could result in even higher quality outputs and broader application potential.

Conclusion

The SEINE model marks a significant advancement in the field of video generation, particularly in the context of creating coherent story-level long videos with smooth and creative transitions. The combination of a random-mask diffusion approach with text-based control addresses a critical gap in current video generation technologies. The experimental results underscore its effectiveness, and its potential applications make it a valuable contribution to industries reliant on high-quality video content.