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Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial…

Computer Vision and Pattern Recognition · Computer Science 2022-06-24 Jonathan Ho , Tim Salimans , Alexey Gritsenko , William Chan , Mohammad Norouzi , David J. Fleet

Advances in technology have led to the development of methods that can create desired visual multimedia. In particular, image generation using deep learning has been extensively studied across diverse fields. In comparison, video…

Computer Vision and Pattern Recognition · Computer Science 2021-06-29 Doyeon Kim , Donggyu Joo , Junmo Kim

Recent advances in diffusion models bring new vitality to visual content creation. However, current text-to-video generation models still face significant challenges such as high training costs, substantial data requirements, and…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Sicong Feng , Jielong Yang , Li Peng

Recent advances in video generation have shown remarkable progress in open-domain settings, yet medical video generation remains largely underexplored. Medical videos are critical for applications such as clinical training, education, and…

Computer Vision and Pattern Recognition · Computer Science 2025-07-09 Rongsheng Wang , Junying Chen , Ke Ji , Zhenyang Cai , Shunian Chen , Yunjin Yang , Benyou Wang

Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sensitive areas such as medical imaging. Medical images feature…

Computer Vision and Pattern Recognition · Computer Science 2025-10-09 Sarah Cechnicka , Matthew Baugh , Weitong Zhang , Mischa Dombrowski , Zhe Li , Johannes C. Paetzold , Candice Roufosse , Bernhard Kainz

The advancement of autonomous driving technologies necessitates increasingly sophisticated methods for understanding and predicting real-world scenarios. Vision language models (VLMs) are emerging as revolutionary tools with significant…

Computer Vision and Pattern Recognition · Computer Science 2024-08-30 Yongjie Fu , Anmol Jain , Xuan Di , Xu Chen , Zhaobin Mo

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 generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on diffusion models, where the effects of motion are modeled in…

Computer Vision and Pattern Recognition · Computer Science 2022-12-02 Kangfu Mei , Vishal M. Patel

Current video generation models produce physically inconsistent motion that violates real-world dynamics. We propose TrajVLM-Gen, a two-stage framework for physics-aware image-to-video generation. First, we employ a Vision Language Model to…

Computer Vision and Pattern Recognition · Computer Science 2025-10-02 Fan Yang , Zhiyang Chen , Yousong Zhu , Xin Li , Jinqiao Wang

Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical images, driving advances in medical image analysis, disease diagnosis, and treatment planning. This chapter explores various deep generative models…

Image and Video Processing · Electrical Eng. & Systems 2024-10-24 Paul Friedrich , Yannik Frisch , Philippe C. Cattin

Sounding Video Generation (SVG) is an audio-video joint generation task challenged by high-dimensional signal spaces, distinct data formats, and different patterns of content information. To address these issues, we introduce a novel…

Computer Vision and Pattern Recognition · Computer Science 2024-10-03 Mingzhen Sun , Weining Wang , Yanyuan Qiao , Jiahui Sun , Zihan Qin , Longteng Guo , Xinxin Zhu , Jing Liu

Image-to-Video (I2V) generation aims to synthesize a video clip according to a given image and condition (e.g., text). The key challenge of this task lies in simultaneously generating natural motions while preserving the original appearance…

Computer Vision and Pattern Recognition · Computer Science 2025-03-04 Jie Tian , Xiaoye Qu , Zhenyi Lu , Wei Wei , Sichen Liu , Yu Cheng

Deep generative models have demonstrated remarkable success in medical image synthesis. However, ensuring conditioning faithfulness and high-quality synthetic images for direct or counterfactual generation remains a challenge. In this work,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-31 Fangrui Huang , Alan Wang , Binxu Li , Bailey Trang , Ridvan Yesiloglu , Tianyu Hua , Wei Peng , Ehsan Adeli

Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality videos from text prompts or single images, personalized…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Yufan Deng , Xun Guo , Yizhi Wang , Jacob Zhiyuan Fang , Angtian Wang , Shenghai Yuan , Yiding Yang , Bo Liu , Haibin Huang , Chongyang Ma

Echocardiography video is a primary modality for diagnosing heart diseases, but the limited data poses challenges for both clinical teaching and machine learning training. Recently, video generative models have emerged as a promising…

Image and Video Processing · Electrical Eng. & Systems 2024-08-01 Junxuan Yu , Rusi Chen , Yongsong Zhou , Yanlin Chen , Yaofei Duan , Yuhao Huang , Han Zhou , Tan Tao , Xin Yang , Dong Ni

Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for medical AI development. Diffusion models can now generate realistic images from text prompts, while recent advancements…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Weixiang Sun , Xiaocao You , Ruizhe Zheng , Zhengqing Yuan , Xiang Li , Lifang He , Quanzheng Li , Lichao Sun

Recent advancements in generative models have revolutionized the field of artificial intelligence, enabling the creation of highly-realistic and detailed images. In this study, we propose a novel Mask Conditional Text-to-Image Generative…

Computer Vision and Pattern Recognition · Computer Science 2024-10-02 Rami Skaik , Leonardo Rossi , Tomaso Fontanini , Andrea Prati

As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation…

Computer Vision and Pattern Recognition · Computer Science 2024-11-13 Linyuan Li , Jianing Qiu , Anujit Saha , Lin Li , Poyuan Li , Mengxian He , Ziyu Guo , Wu Yuan

Recently, diffusion models like StableDiffusion have achieved impressive image generation results. However, the generation process of such diffusion models is uncontrollable, which makes it hard to generate videos with continuous and…

Computer Vision and Pattern Recognition · Computer Science 2023-08-04 Zhihao Hu , Dong Xu

Recent advances in large generative models have shown that simple autoregressive formulations, when scaled appropriately, can exhibit strong zero-shot generalization across domains. Motivated by this trend, we investigate whether…

Computer Vision and Pattern Recognition · Computer Science 2026-04-27 Yuxiang Lai , Jike Zhong , Ming Li , Yuheng Li , Xiaofeng Yang