arXiv:2407.07111v1 Announce Type: cross
Abstract: The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making “what you want is what you see” a reality. Among these, video editing has gained substantial attention and seen a swift rise in research activity, necessitating a comprehensive and systematic review of the existing literature. This paper reviews diffusion model-based video editing techniques, including theoretical foundations and practical applications. We begin by overviewing the mathematical formulation and image domain’s key methods. Subsequently, we categorize video editing approaches by the inherent connections of their core technologies, depicting evolutionary trajectory. This paper also dives into novel applications, including point-based editing and pose-guided human video editing. Additionally, we present a comprehensive comparison using our newly introduced V2VBench. Building on the progress achieved to date, the paper concludes with ongoing challenges and potential directions for future research.
Expert Commentary: Advances in Diffusion Model-Based Video Editing Techniques
Video editing has become a crucial component in the multimedia information systems field, enabling users to create visually appealing and informative content. The rapid development of diffusion models (DMs) has significantly enhanced the capabilities of image and video applications, allowing users to see exactly what they want. This paper provides a comprehensive and systematic review of the existing literature on diffusion model-based video editing techniques, shedding light on their theoretical foundations, practical applications, and future directions for research.
One of the key strengths of this paper is its multi-disciplinary nature. Video editing techniques in diffusion models draw upon concepts from various fields such as computer vision, image processing, and machine learning. By exploring the mathematical formulation and key methods in the image domain, the paper establishes the theoretical foundations of diffusion model-based video editing techniques. This interdisciplinary approach is crucial for understanding the complex algorithms underlying these techniques and their potential applications.
The paper categorizes video editing approaches based on the inherent connections of their core technologies, providing a comprehensive overview of the evolutionary trajectory in this field. This categorization aids in understanding the different techniques employed and their relative strengths and limitations. Furthermore, the paper goes beyond traditional video editing techniques and explores novel applications such as point-based editing and pose-guided human video editing. These innovative applications demonstrate the versatility of diffusion model-based video editing techniques and their potential impact on various domains, including entertainment, advertising, and education.
In addition, the paper introduces V2VBench, a comprehensive comparison framework that allows for a quantitative evaluation of different diffusion model-based video editing techniques. This framework enables researchers and practitioners to objectively assess the performance of these techniques, facilitating benchmarking and further advancements in the field.
When considering the wider field of multimedia information systems, diffusion model-based video editing techniques play a significant role in enhancing the user experience. These techniques contribute to the creation of visually stunning animations, artificial realities, augmented realities, and virtual realities. By utilizing diffusion models, video editors can manipulate videos in a way that seamlessly integrates with these multimedia systems. This integration opens up new avenues for immersive storytelling, interactive experiences, and realistic simulations.
However, despite the progress achieved so far, several challenges remain in diffusion model-based video editing. These include improving the efficiency and scalability of existing algorithms, developing techniques for handling complex video scenes, and addressing the ethical considerations surrounding the manipulation of video content. These challenges present exciting opportunities for future research, as they push the boundaries of current techniques and pave the way for innovative solutions.
In conclusion, this paper provides a comprehensive review of diffusion model-based video editing techniques, highlighting their theoretical foundations, practical applications, and future directions. With its multi-disciplinary approach and emphasis on novel applications, the paper significantly contributes to the wider field of multimedia information systems, making it a valuable resource for researchers, practitioners, and enthusiasts in this field.