A Survey on Video Diffusion Models (2310.10647v2)

Abstract: The recent wave of AI-generated content (AIGC) has witnessed substantial success in computer vision, with the diffusion model playing a crucial role in this achievement. Due to their impressive generative capabilities, diffusion models are gradually superseding methods based on GANs and auto-regressive Transformers, demonstrating exceptional performance not only in image generation and editing, but also in the realm of video-related research. However, existing surveys mainly focus on diffusion models in the context of image generation, with few up-to-date reviews on their application in the video domain. To address this gap, this paper presents a comprehensive review of video diffusion models in the AIGC era. Specifically, we begin with a concise introduction to the fundamentals and evolution of diffusion models. Subsequently, we present an overview of research on diffusion models in the video domain, categorizing the work into three key areas: video generation, video editing, and other video understanding tasks. We conduct a thorough review of the literature in these three key areas, including further categorization and practical contributions in the field. Finally, we discuss the challenges faced by research in this domain and outline potential future developmental trends. A comprehensive list of video diffusion models studied in this survey is available at https://github.com/ChenHsing/Awesome-Video-Diffusion-Models.

Comprehensive Review of Video Diffusion Models in AI-Generated Content

The paper "A Survey on Video Diffusion Models" by Zhen Xing et al. offers an extensive survey on the application of diffusion models within the domain of video generation and editing in the context of AI-generated content (AIGC). This review aims to provide a formal analysis of the methodologies covered in the paper, emphasizing the progress, challenges, and future prospects in the field.

The diffusion model, a probabilistic generative model, has become a pivotal approach outperforming GANs and auto-regressive Transformers in tasks such as image and video generation. Despite the wealth of literature on image diffusion models, video diffusion models have not been extensively reviewed until now. As videos present a richer medium with dynamic content, understanding their generation using diffusion models is crucial for advancing AIGC.

Key Areas in Video Diffusion Models

1. Video Generation with Text Condition:
   • Training-based Approaches: The paper details various innovations in training-based methods aimed at optimizing video diffusion models, emphasizing improvements in temporal modeling and noise prior exploration. Methods like VDM and Imagen Video have introduced hierarchical and multi-stage processes to enhance video quality and temporal coherence.
   • Training-free Approaches: Methods like Text2Video-Zero seek to reduce training costs by adapting pre-trained text-to-image models for video generation, highlighting an efficient alternative to data-heavy training pipelines.
2. Video Generation with Other Conditions:
   • Pose-guided, Motion-guided, and Sound-guided Video Generation: These approaches demonstrate the adaptability of diffusion models to different modalities, showcasing their ability to incorporate varied inputs like pose sequences, motion strokes, and audio features into the video generation process.
   • Multi-modal Integration: Techniques such as MovieFactory illustrate the potential for blending multiple modalities, opening new avenues for creative content generation.
3. Unconditional Video Generation:
   • The exploration of unconditional generation further argues the adaptability of diffusion models to generate diverse and coherent video content without explicit conditions, marked by strategies in models like VIDM and VDT.
4. Video Completion:
   • The paper also covers tasks related to video completion, such as video enhancement and prediction, underscoring the practical applications of diffusion models in filling and predicting video content.

Benchmark Results

The paper highlights benchmark comparisons across popular datasets, demonstrating the efficacy of diffusion models in tasks such as zero-shot and fine-tuned video generation. The reported metrics, including FVD, IS, and CLIPSIM, provide valuable insights into model performance across different datasets and conditions.

Future Challenges and Directions

Despite the significant advancements, several challenges remain: the need for large-scale video-text datasets, the high training and inference costs, the absence of comprehensive evaluation methods, and current model limitations in handling intricate temporal and spatial relationships.

The paper calls for efficient training strategies, more extensive and high-quality datasets, and improved evaluation benchmarks to achieve more realistic and coherent video generation. There is also a need for further development to overcome existing model incapacities, particularly in maintaining consistency and alignment across frames.

Conclusion

This survey serves as a foundational contribution to understanding and advancing video diffusion models within AIGC. By identifying key trends, methodologies, and challenges, the paper provides a roadmap for future research in enhancing the scope and capability of diffusion models in video-related tasks. This work will undoubtedly serve as a catalyst for further exploration and innovation in video synthesis and editing using diffusion models.