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Related papers: Evolution of Video Generative Foundations

200 papers

We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Gyeongrok Oh , Jaehwan Jeong , Sieun Kim , Wonmin Byeon , Jinkyu Kim , Sungwoong Kim , Sangpil Kim

Multimodal generative models are rapidly evolving, leading to a surge in the generation of realistic video and audio that offers exciting possibilities but also serious risks. Deepfake videos, which can convincingly impersonate individuals,…

Computer Vision and Pattern Recognition · Computer Science 2024-08-22 Hannah Lee , Changyeon Lee , Kevin Farhat , Lin Qiu , Steve Geluso , Aerin Kim , Oren Etzioni

Recent advancements in generative models have significantly facilitated the development of personalized content creation. Given a small set of images with user-specific concept, personalized image generation allows to create images that…

Computer Vision and Pattern Recognition · Computer Science 2025-02-19 Yuxiang Wei , Yiheng Zheng , Yabo Zhang , Ming Liu , Zhilong Ji , Lei Zhang , Wangmeng Zuo

Video-based world models have recently garnered increasing attention for their ability to synthesize diverse and dynamic visual environments. In this paper, we focus on shared world modeling, where a model generates multiple videos from a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-03 Fan Wu , Jiacheng Wei , Ruibo Li , Yi Xu , Junyou Li , Deheng Ye , Guosheng Lin

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

Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Chenyang Si , Weichen Fan , Zhengyao Lv , Ziqi Huang , Yu Qiao , Ziwei Liu

Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly,…

Computer Vision and Pattern Recognition · Computer Science 2024-02-01 Xiaoyu Li , Qi Zhang , Di Kang , Weihao Cheng , Yiming Gao , Jingbo Zhang , Zhihao Liang , Jing Liao , Yan-Pei Cao , Ying Shan

Video generation is a rapidly advancing research area, garnering significant attention due to its broad range of applications. One critical aspect of this field is the generation of long-duration videos, which presents unique challenges and…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Chengxuan Li , Di Huang , Zeyu Lu , Yang Xiao , Qingqi Pei , Lei Bai

The proliferation of AI-Generated Content (AIGC), especially deepfake videos, poses a severe threat to social trust by enabling fraud, privacy violations and disinformation. Existing AI-generated video detection (AGVD) benchmarks focus on…

Computer Vision and Pattern Recognition · Computer Science 2026-05-08 Xingming Liao , Meiyu Zeng , Canyu Chen , Nankai Lin , Zhuowei Wang , Aimin Yang

Recently, video generation techniques have advanced rapidly. Given the popularity of video content on social media platforms, these models intensify concerns about the spread of fake information. Therefore, there is a growing demand for…

Computer Vision and Pattern Recognition · Computer Science 2024-08-23 Haoxing Chen , Yan Hong , Zizheng Huang , Zhuoer Xu , Zhangxuan Gu , Yaohui Li , Jun Lan , Huijia Zhu , Jianfu Zhang , Weiqiang Wang , Huaxiong Li

Generating semantically and temporally aligned audio content in accordance with video input has become a focal point for researchers, particularly following the remarkable breakthrough in text-to-video generation. In this work, we aim to…

Sound · Computer Science 2025-03-12 Manjie Xu , Chenxing Li , Xinyi Tu , Yong Ren , Rilin Chen , Yu Gu , Wei Liang , Dong Yu

The evolution of video generation from text, from animating MNIST to simulating the world with Sora, has progressed at a breakneck speed. Here, we systematically discuss how far text-to-video generation technology supports essential…

The rapid advancement of generative AI technology, particularly video generative AI (Video GenAI), has opened new possibilities for K-12 education by enabling the creation of dynamic, customized, and high-quality visual content. Despite its…

Computers and Society · Computer Science 2025-03-12 Unggi Lee , Yeil Jeong , Seungha Kim , Yoorim Son , Gyuri Byun , Hyeoncheol Kim , Cheolil Lim

This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transformer paradigm, Wan achieves significant advancements in…

The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models…

Long video generation remains a challenging and compelling topic in computer vision. Diffusion based models, among the various approaches to video generation, have achieved state of the art quality with their iterative denoising procedures.…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Siyang Zhang , Harry Yang , Ser-Nam Lim

GANs are able to perform generation and manipulation tasks, trained on a single video. However, these single video GANs require unreasonable amount of time to train on a single video, rendering them almost impractical. In this paper we…

Computer Vision and Pattern Recognition · Computer Science 2022-05-13 Niv Haim , Ben Feinstein , Niv Granot , Assaf Shocher , Shai Bagon , Tali Dekel , Michal Irani

Recent advances in diffusion-based generation techniques enable AI models to produce highly realistic videos, heightening the need for reliable detection mechanisms. However, existing detection methods provide only limited exploration of…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Wenhan Chen , Sezer Karaoglu , Theo Gevers

The development of AI-Generated Video (AIGV) technology has been remarkable in recent years, significantly transforming the paradigm of video content production. However, AIGVs still suffer from noticeable visual quality defects, such as…

Computer Vision and Pattern Recognition · Computer Science 2025-06-13 Zelu Qi , Ping Shi , Chaoyang Zhang , Shuqi Wang , Fei Zhao , Da Pan , Zefeng Ying

Promotional videos are rapidly becoming a popular medium for persuading people to change their behaviours in many settings (e.g., online shopping, social enterprise initiatives). Today, such videos are often produced by professionals, which…

Multimedia · Computer Science 2021-12-20 Chang Liu , Han Yu