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Video diffusion models have made substantial progress in various video generation applications. However, training models for long video generation tasks require significant computational and data resources, posing a challenge to developing…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Yu Lu , Yuanzhi Liang , Linchao Zhu , Yi Yang

Recent advancements in diffusion models have revolutionized video generation, enabling the creation of high-quality, temporally consistent videos. However, generating high frame-rate (FPS) videos remains a significant challenge due to…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Geunmin Hwang , Hyun-kyu Ko , Younghyun Kim , Seungryong Lee , Eunbyung Park

Video diffusion models have recently achieved remarkable results in video generation. Despite their encouraging performance, most of these models are mainly designed and trained for short video generation, leading to challenges in…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Zhuoling Li , Hossein Rahmani , Qiuhong Ke , Jun Liu

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

Generating long and consistent videos has emerged as a significant yet challenging problem. While most existing diffusion-based video generation models, derived from image generation models, demonstrate promising performance in generating…

Computer Vision and Pattern Recognition · Computer Science 2024-04-30 Yichen Ouyang , jianhao Yuan , Hao Zhao , Gaoang Wang , Bo zhao

Diffusion generative models have recently become a powerful technique for creating and modifying high-quality, coherent video content. This survey provides a comprehensive overview of the critical components of diffusion models for video…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Andrew Melnik , Michal Ljubljanac , Cong Lu , Qi Yan , Weiming Ren , Helge Ritter

AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length…

Computer Vision and Pattern Recognition · Computer Science 2023-03-21 Yingqing He , Tianyu Yang , Yong Zhang , Ying Shan , Qifeng Chen

Diffusion models have revolutionized image and video generation, achieving unprecedented visual quality. However, their reliance on transformer architectures incurs prohibitively high computational costs, particularly when extending…

Computer Vision and Pattern Recognition · Computer Science 2025-10-03 Justin Cui , Jie Wu , Ming Li , Tao Yang , Xiaojie Li , Rui Wang , Andrew Bai , Yuanhao Ban , Cho-Jui Hsieh

Video generation models hold substantial potential in areas such as filmmaking. However, current video diffusion models need high computational costs and produce suboptimal results due to extreme complexity of video generation task. In this…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Wenhao Li , Yichao Cao , Xiu Su , Xi Lin , Shan You , Mingkai Zheng , Yi Chen , Chang Xu

In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion. We leverage an…

Computer Vision and Pattern Recognition · Computer Science 2023-09-08 Xin Li , Wenqing Chu , Ye Wu , Weihang Yuan , Fanglong Liu , Qi Zhang , Fu Li , Haocheng Feng , Errui Ding , Jingdong Wang

Recent advances in video generation models have enabled high-quality short video generation from text prompts. However, extending these models to longer videos remains a significant challenge, primarily due to degraded temporal consistency…

Computer Vision and Pattern Recognition · Computer Science 2025-07-02 Yu Lu , Yi Yang

Creating high-fidelity, coherent long videos is a sought-after aspiration. While recent video diffusion models have shown promising potential, they still grapple with spatiotemporal inconsistencies and high computational resource demands.…

Computer Vision and Pattern Recognition · Computer Science 2025-01-13 Yongjia Ma , Junlin Chen , Donglin Di , Qi Xie , Lei Fan , Wei Chen , Xiaofei Gou , Na Zhao , Xun Yang

Generating realistic animated videos from static images is an important area of research in computer vision. Methods based on physical simulation and motion prediction have achieved notable advances, but they are often limited to specific…

Computer Vision and Pattern Recognition · Computer Science 2024-05-29 Qiang Wang , Minghua Liu , Junjun Hu , Fan Jiang , Mu Xu

Video generation has made remarkable progress in recent years, especially since the advent of the video diffusion models. Many video generation models can produce plausible synthetic videos, e.g., Stable Video Diffusion (SVD). However, most…

Computer Vision and Pattern Recognition · Computer Science 2024-06-04 Shaoshu Yang , Yong Zhang , Xiaodong Cun , Ying Shan , Ran He

Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives. Traditional methods like morphing often lack artistic appeal and require specialized skills, limiting their effectiveness.…

Computer Vision and Pattern Recognition · Computer Science 2024-08-27 Rui Zhang , Yaosen Chen , Yuegen Liu , Wei Wang , Xuming Wen , Hongxia Wang

We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained for 2D image synthesis have been turned into…

Computer Vision and Pattern Recognition · Computer Science 2023-11-28 Andreas Blattmann , Tim Dockhorn , Sumith Kulal , Daniel Mendelevitch , Maciej Kilian , Dominik Lorenz , Yam Levi , Zion English , Vikram Voleti , Adam Letts , Varun Jampani , Robin Rombach

Diffusion models have achieved impressive performance in video generation, but their iterative denoising process remains computationally expensive due to the large number of tokens processed at each timestep. Recently, progressive…

Computer Vision and Pattern Recognition · Computer Science 2026-05-21 Shikang Zheng , Jingkai Huang , Jiacheng Liu , Guantao Chen , Lixuan , Yuqi Lin , Peiliang Cai , Linfeng Zhang

Generating long-duration videos has always been a significant challenge due to the inherent complexity of spatio-temporal domain and the substantial GPU memory demands required to calculate huge size tensors. While diffusion based…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Siyang Zhang , Ser-Nam Lim

Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control. These advancements hold great promise for improving surgical education by enabling…

Computer Vision and Pattern Recognition · Computer Science 2024-09-26 Joseph Cho , Samuel Schmidgall , Cyril Zakka , Mrudang Mathur , Dhamanpreet Kaur , Rohan Shad , William Hiesinger

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Andreas Blattmann , Robin Rombach , Huan Ling , Tim Dockhorn , Seung Wook Kim , Sanja Fidler , Karsten Kreis
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