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Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview…

Machine Learning · Computer Science 2025-09-30 Ling Yang , Zhilong Zhang , Yang Song , Shenda Hong , Runsheng Xu , Yue Zhao , Wentao Zhang , Bin Cui , Ming-Hsuan Yang

Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion models primarily focus on incorporating text-visual…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Ling Yang , Zhilong Zhang , Zhaochen Yu , Jingwei Liu , Minkai Xu , Stefano Ermon , Bin Cui

Building on the momentum of image generation diffusion models, there is an increasing interest in video-based diffusion models. However, video generation poses greater challenges due to its higher-dimensional nature, the scarcity of…

Computer Vision and Pattern Recognition · Computer Science 2024-11-01 Aimon Rahman , Malsha V. Perera , Vishal M. Patel

Video prediction is commonly referred to as forecasting future frames of a video sequence provided several past frames thereof. It remains a challenging domain as visual scenes evolve according to complex underlying dynamics, such as the…

Computer Vision and Pattern Recognition · Computer Science 2021-05-12 Hafez Farazi , Jan Nogga , Sven Behnke

Diffusion models have achieved remarkable success in generating high quality image and video data. More recently, they have also been used for image compression with high perceptual quality. In this paper, we present a novel approach to…

Image and Video Processing · Electrical Eng. & Systems 2024-02-15 Bohan Li , Yiming Liu , Xueyan Niu , Bo Bai , Lei Deng , Deniz Gündüz

Perceptual studies demonstrate that conditional diffusion models excel at reconstructing video content aligned with human visual perception. Building on this insight, we propose a video compression framework that leverages conditional…

Computer Vision and Pattern Recognition · Computer Science 2025-09-26 Fangqiu Yi , Jingyu Xu , Jiawei Shao , Chi Zhang , Xuelong Li

World models, which predict future transitions from past observation and action sequences, have shown great promise for improving data efficiency in sequential decision-making. However, existing world models often require extensive…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Siqiao Huang , Jialong Wu , Qixing Zhou , Shangchen Miao , Mingsheng Long

Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality, spatially consistent new views. While recent…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Jeong-gi Kwak , Erqun Dong , Yuhe Jin , Hanseok Ko , Shweta Mahajan , Kwang Moo Yi

Generating visual instructions in a given context is essential for developing interactive world simulators. While prior works address this problem through either text-guided image manipulation or video prediction, these tasks are typically…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Yujiang Pu , Zhanbo Huang , Vishnu Boddeti , Yu Kong

Realistic temporal dynamics are crucial for many video generation, processing and modelling applications, e.g. in computational fluid dynamics, weather prediction, or long-term climate simulations. Video diffusion models (VDMs) are the…

Machine Learning · Computer Science 2025-05-16 Philipp Hess , Maximilian Gelbrecht , Christof Schötz , Michael Aich , Yu Huang , Shangshang Yang , Niklas Boers

We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling. Previous approaches have suffered from the discrepancy between discrete data and continuous modeling.…

Machine Learning · Computer Science 2024-10-31 Yuxuan Gu , Xiaocheng Feng , Lei Huang , Yingsheng Wu , Zekun Zhou , Weihong Zhong , Kun Zhu , Bing Qin

Diffusion-based methods can generate realistic images and videos, but they struggle to edit existing objects in a video while preserving their appearance over time. This prevents diffusion models from being applied to natural video editing…

Computer Vision and Pattern Recognition · Computer Science 2023-08-21 Wenhao Chai , Xun Guo , Gaoang Wang , Yan Lu

We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments. We introduce a generative model that can at test-time sample…

Computer Vision and Pattern Recognition · Computer Science 2022-12-19 William Harvey , Saeid Naderiparizi , Vaden Masrani , Christian Weilbach , Frank Wood

Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos. Existing methods struggle to distinguishing between motion and structure, particularly in scenarios where camera poses are either unknown…

Computer Vision and Pattern Recognition · Computer Science 2024-01-12 Chaoyang Wang , Peiye Zhuang , Aliaksandr Siarohin , Junli Cao , Guocheng Qian , Hsin-Ying Lee , Sergey Tulyakov

While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to explicitly consider one of the key differences between videos…

Computer Vision and Pattern Recognition · Computer Science 2024-07-31 Jingyun Liang , Yuchen Fan , Kai Zhang , Radu Timofte , Luc Van Gool , Rakesh Ranjan

Diffusion models have emerged as a powerful paradigm in video synthesis tasks including prediction, generation, and interpolation. Due to the limitation of the computational budget, existing methods usually implement conditional diffusion…

Computer Vision and Pattern Recognition · Computer Science 2023-06-06 Siyuan Yang , Lu Zhang , Yu Liu , Zhizhuo Jiang , You He

Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed. These methods typically rely on signal prediction theory to enhance compression performance by…

Computer Vision and Pattern Recognition · Computer Science 2023-08-31 Lv Tang , Xinfeng Zhang , Gai Zhang , Xiaoqi Ma

Image generation and editing have seen a great deal of advancements with the rise of large-scale diffusion models that allow user control of different modalities such as text, mask, depth maps, etc. However, controlled editing of videos…

Computer Vision and Pattern Recognition · Computer Science 2024-06-04 AmirHossein Zamani , Amir G. Aghdam , Tiberiu Popa , Eugene Belilovsky

We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high definition videos using a base video generation model and a sequence of…

Computer Vision and Pattern Recognition · Computer Science 2022-10-06 Jonathan Ho , William Chan , Chitwan Saharia , Jay Whang , Ruiqi Gao , Alexey Gritsenko , Diederik P. Kingma , Ben Poole , Mohammad Norouzi , David J. Fleet , Tim Salimans

Given an input video of a person and a new garment, the objective of this paper is to synthesize a new video where the person is wearing the specified garment while maintaining spatiotemporal consistency. Although significant advances have…

Computer Vision and Pattern Recognition · Computer Science 2024-12-19 Hung Nguyen , Quang Qui-Vinh Nguyen , Khoi Nguyen , Rang Nguyen