A Survey on Video Diffusion Models
Zhen Xing Qijun Feng Haoran Chen Qi Dai Han Hu Hang Xu Zuxuan Wu Yu-Gang Jiang
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.