Home Community A Comprehensive Review of Video Diffusion Models within the Artificial Intelligence Generated Content (AIGC)

A Comprehensive Review of Video Diffusion Models within the Artificial Intelligence Generated Content (AIGC)

A Comprehensive Review of Video Diffusion Models within the Artificial Intelligence Generated Content (AIGC)

Artificial Intelligence is booming, and so is its sub-field, i.e., the domain of Computer Vision. From researchers and academics to scholars, it’s getting plenty of attention and is making a big effect on plenty of different industries and applications, like computer graphics, art and design, medical imaging, etc. Diffusion models have been the predominant technique for image production amongst the assorted approaches. They’ve outperformed strategies based on generative adversarial networks (GANs) and auto-regressive Transformers. These diffusion-based techniques are preferred because they’re controllable, can create a wide selection of outputs, and may produce extremely realistic images. They’ve found use in a wide range of computer vision tasks, including 3D generation, video synthesis, dense prediction, and image editing.

The diffusion model has been crucial to the considerable advancements in computer vision, as evidenced by the recent boom in AI-generated content (AIGC). These models will not be only achieving remarkable leads to image generation and editing, but also they are leading the way in which in research connected to videos. While surveys addressing diffusion models within the context of picture production have been published, there are few recent reviews that examine their use within the video domain. Recent work provides an intensive evaluation of video diffusion models within the AIGC era with a view to close this gap.

In a recent research paper, a team of researchers has highlighted how crucial diffusion models are in showing remarkable generative powers, surpassing alternative techniques, and exhibiting noteworthy performance in image generation and editing, in addition to in the sector of video-related research. The paper’s predominant focus is an intensive investigation of video diffusion models within the context of AIGC. It’s separated into three predominant sections: duties related to creating, editing, and comprehending videos. The report summarises the sensible contributions made by researchers, reviews the body of literature that has already been written in these fields, and organizes the work.

The paper has also shared the difficulties that researchers on this field face. It also delineates prospective avenues for future research and development in the sector of video diffusion models and offers perspectives on potential future directions for the world in addition to challenges that also should be solved.

The first contributions of the research paper are as follows.

  1. Methodical monitoring and synthesis of current research on video dissemination models has been included, reminiscent of a variety of topics like video creation, editing, and comprehension.
  1. Background information and pertinent data on video diffusion models have been introduced, together with datasets, assessment measures, and problem definitions.
  1. A summary of probably the most influential works on the subject, specializing in common technical information, has been shared.
  1. An in-depth examination and contrast of video-generating benchmarks and settings, addressing a critical need within the literature, has also been shared.

To sum up, this study is a useful tool for anyone interested by probably the most recent developments in video diffusion models within the context of AIGC. It also acknowledges the necessity for added studies and reviews within the video domain, emphasizing the importance of diffusion models within the context of computer vision. The study provides an intensive overview of the subject by classifying and assessing previous work, highlighting potential future trends and obstacles for further investigation.

Try the Paper and Github link. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to affix our 32k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the most recent AI research news, cool AI projects, and more.

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Tanya Malhotra is a final yr undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and demanding pondering, together with an ardent interest in acquiring latest skills, leading groups, and managing work in an organized manner.

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