Summary of Video Diffusion Models: a Survey, by Andrew Melnik et al.
Video Diffusion Models: A Survey
by Andrew Melnik, Michal Ljubljanac, Cong Lu, Qi Yan, Weiming Ren, Helge Ritter
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey provides a comprehensive overview of diffusion generative models for video generation, covering their applications, architectural design, and temporal dynamics modeling. The paper discusses core principles, mathematical formulations, and various architectural choices to maintain temporal consistency. It also presents a taxonomy of applications based on input modalities like text prompts, images, videos, and audio signals. The survey summarizes recent developments in training and evaluation practices, including diverse video and image datasets and various evaluation metrics. Furthermore, it examines ongoing challenges, such as generating longer videos and managing computational costs, and offers insights into potential future directions for the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion generative models can create high-quality, coherent video content. This paper looks at how these models work, what they’re used for, and why they matter. It covers the core ideas behind diffusion models, different ways to build them, and how they apply to various tasks like generating videos from text or images. |
Keywords
» Artificial intelligence » Diffusion
