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Multi-turn compositional image generation (M-CIG) is a challenging task that aims to iteratively manipulate a reference image given a modification text. While most of the existing methods for M-CIG are based on generative adversarial…

Computer Vision and Pattern Recognition · Computer Science 2023-11-15 Chao Wang

Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in…

Computer Vision and Pattern Recognition · Computer Science 2022-12-09 Ruihan Yang , Prakhar Srivastava , Stephan Mandt

In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training…

Computer Vision and Pattern Recognition · Computer Science 2023-03-23 Shengming Yin , Chenfei Wu , Huan Yang , Jianfeng Wang , Xiaodong Wang , Minheng Ni , Zhengyuan Yang , Linjie Li , Shuguang Liu , Fan Yang , Jianlong Fu , Gong Ming , Lijuan Wang , Zicheng Liu , Houqiang Li , Nan Duan

Diffusion models are successful for synthesizing high-quality videos but are limited to generating short clips (e.g., 2-10 seconds). Synthesizing sustained footage (e.g. over minutes) still remains an open research question. In this paper,…

Computer Vision and Pattern Recognition · Computer Science 2025-07-09 Sihyun Yu , Meera Hahn , Dan Kondratyuk , Jinwoo Shin , Agrim Gupta , José Lezama , Irfan Essa , David Ross , Jonathan Huang

Text-to-video diffusion models generate realistic videos, but often fail on prompts requiring fine-grained compositional understanding, such as relations between entities, attributes, actions, and motion directions. We hypothesize that…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Ariel Shaulov , Eitan Shaar , Amit Edenzon , Gal Chechik , Lior Wolf

Diffusion-based video depth estimation methods have achieved remarkable success with strong generalization ability. However, predicting depth for long videos remains challenging. Existing methods typically split videos into overlapping…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Yue-Jiang Dong , Wang Zhao , Jiale Xu , Ying Shan , Song-Hai Zhang

Generative video modeling has made significant strides, yet ensuring structural and temporal consistency over long sequences remains a challenge. Current methods predominantly rely on RGB signals, leading to accumulated errors in object…

Computer Vision and Pattern Recognition · Computer Science 2025-08-22 Zhiheng Liu , Xueqing Deng , Shoufa Chen , Angtian Wang , Qiushan Guo , Mingfei Han , Zeyue Xue , Mengzhao Chen , Ping Luo , Linjie Yang

Controllable ultra-long video generation is a fundamental yet challenging task. Although existing methods are effective for short clips, they struggle to scale due to issues such as temporal inconsistency and visual degradation. In this…

Computer Vision and Pattern Recognition · Computer Science 2025-08-06 Jianxiong Gao , Zhaoxi Chen , Xian Liu , Jianfeng Feng , Chenyang Si , Yanwei Fu , Yu Qiao , Ziwei Liu

Although diffusion-based zero-shot image restoration and enhancement methods have achieved great success, applying them to video restoration or enhancement will lead to severe temporal flickering. In this paper, we propose the first…

Computer Vision and Pattern Recognition · Computer Science 2026-01-30 Cong Cao , Huanjing Yue , Shangbin Xie , Xin Liu , Jingyu Yang

Video generation has drawn significant interest recently, pushing the development of large-scale models capable of producing realistic videos with coherent motion. Due to memory constraints, these models typically generate short video…

Computer Vision and Pattern Recognition · Computer Science 2024-07-23 Idan Kligvasser , Regev Cohen , George Leifman , Ehud Rivlin , Michael Elad

Recent advancements in 3D generation are predominantly propelled by improvements in 3D-aware image diffusion models. These models are pretrained on Internet-scale image data and fine-tuned on massive 3D data, offering the capability of…

Computer Vision and Pattern Recognition · Computer Science 2024-10-03 Zeyu Yang , Zijie Pan , Chun Gu , Li Zhang

Video denoising aims to recover high-quality frames from the noisy video. While most existing approaches adopt convolutional neural networks~(CNNs) to separate the noise from the original visual content, however, CNNs focus on local…

Computer Vision and Pattern Recognition · Computer Science 2023-01-18 Wulian Yun , Mengshi Qi , Chuanming Wang , Huiyuan Fu , Huadong Ma

Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics. In this paper, we delve deep into the noise…

Computer Vision and Pattern Recognition · Computer Science 2024-07-26 Tianxing Wu , Chenyang Si , Yuming Jiang , Ziqi Huang , Ziwei Liu

In this work, we investigate the capability of generating images from pre-trained diffusion models at much higher resolutions than the training image sizes. In addition, the generated images should have arbitrary image aspect ratios. When…

Computer Vision and Pattern Recognition · Computer Science 2023-10-12 Yingqing He , Shaoshu Yang , Haoxin Chen , Xiaodong Cun , Menghan Xia , Yong Zhang , Xintao Wang , Ran He , Qifeng Chen , Ying Shan

Real-world applications like video gaming and virtual reality often demand the ability to model 3D scenes that users can explore along custom camera trajectories. While significant progress has been made in generating 3D objects from text…

Computer Vision and Pattern Recognition · Computer Science 2025-06-05 Tianyu Huang , Wangguandong Zheng , Tengfei Wang , Yuhao Liu , Zhenwei Wang , Junta Wu , Jie Jiang , Hui Li , Rynson W. H. Lau , Wangmeng Zuo , Chunchao Guo

Without incurring significant computational overhead, train-free long video generation aims to enable foundation video generation models to produce longer videos. Frame-level autoregressive frameworks, e.g., FIFO-diffusion, offer the…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 X. Feng , J. Zhu , M. Wu , C. Chen , F. Mao , H. Guo , J. Wu , X. Chu , K. Huang

Current frontier video diffusion models have demonstrated remarkable results at generating high-quality videos. However, they can only generate short video clips, normally around 10 seconds or 240 frames, due to computation limitations…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Desai Xie , Zhan Xu , Yicong Hong , Hao Tan , Difan Liu , Feng Liu , Arie Kaufman , Yang Zhou

Color plays an important role in human visual perception, reflecting the spectrum of objects. However, the existing infrared and visible image fusion methods rarely explore how to handle multi-spectral/channel data directly and achieve high…

Computer Vision and Pattern Recognition · Computer Science 2024-10-28 Jun Yue , Leyuan Fang , Shaobo Xia , Yue Deng , Jiayi Ma

Text-to-Video generation, which utilizes the provided text prompt to generate high-quality videos, has drawn increasing attention and achieved great success due to the development of diffusion models recently. Existing methods mainly rely…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Zirui Pan , Xin Wang , Yipeng Zhang , Hong Chen , Kwan Man Cheng , Yaofei Wu , Wenwu Zhu

Efficient video transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality video transmission over a bandwidth-constrained noisy wireless…

Image and Video Processing · Electrical Eng. & Systems 2025-03-03 Nan Li , Yansha Deng , Dusit Niyato