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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

Recent advances in large reconstruction and generative models have significantly improved scene reconstruction and novel view generation. However, due to compute limitations, each inference with these large models is confined to a small…

Computer Vision and Pattern Recognition · Computer Science 2025-04-15 Shangjin Zhai , Zhichao Ye , Jialin Liu , Weijian Xie , Jiaqi Hu , Zhen Peng , Hua Xue , Danpeng Chen , Xiaomeng Wang , Lei Yang , Nan Wang , Haomin Liu , Guofeng Zhang

Recovering 3D scenes from sparse views is a challenging task due to its inherent ill-posed problem. Conventional methods have developed specialized solutions (e.g., geometry regularization or feed-forward deterministic model) to mitigate…

Computer Vision and Pattern Recognition · Computer Science 2025-04-04 Hanyang Wang , Fangfu Liu , Jiawei Chi , Yueqi Duan

While video generation models excel at producing high-quality monocular videos, generating 3D stereoscopic and spatial videos for immersive applications remains an underexplored challenge. We present a pose-free and training-free method…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Peng Dai , Feitong Tan , Qiangeng Xu , Yihua Huang , David Futschik , Ruofei Du , Sean Fanello , Yinda Zhang , Xiaojuan Qi

We introduce Reangle-A-Video, a unified framework for generating synchronized multi-view videos from a single input video. Unlike mainstream approaches that train multi-view video diffusion models on large-scale 4D datasets, our method…

Computer Vision and Pattern Recognition · Computer Science 2025-09-11 Hyeonho Jeong , Suhyeon Lee , Jong Chul Ye

Recent video inpainting algorithms integrate flow-based pixel propagation with transformer-based generation to leverage optical flow for restoring textures and objects using information from neighboring frames, while completing masked…

Computer Vision and Pattern Recognition · Computer Science 2025-01-20 Xiaowen Li , Haolan Xue , Peiran Ren , Liefeng Bo

We present DiffIR2VR-Zero, a zero-shot framework that enables any pre-trained image restoration diffusion model to perform high-quality video restoration without additional training. While image diffusion models have shown remarkable…

Computer Vision and Pattern Recognition · Computer Science 2026-01-01 Chang-Han Yeh , Hau-Shiang Shiu , Chin-Yang Lin , Zhixiang Wang , Chi-Wei Hsiao , Ting-Hsuan Chen , Yu-Lun Liu

Customized generation using diffusion models has made impressive progress in image generation, but remains unsatisfactory in the challenging video generation task, as it requires the controllability of both subjects and motions. To that…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Yujie Wei , Shiwei Zhang , Zhiwu Qing , Hangjie Yuan , Zhiheng Liu , Yu Liu , Yingya Zhang , Jingren Zhou , Hongming Shan

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

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

In this paper, we propose the first diffusion-based all-in-one video restoration method that utilizes the power of a pre-trained Stable Diffusion and a fine-tuned ControlNet. Our method can restore various types of video degradation with a…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Yizhou Li , Zihua Liu , Yusuke Monno , Masatoshi Okutomi

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

Recent advances in video generation have enabled the synthesis of high-quality and visually realistic clips using diffusion transformer models. However, most existing approaches operate purely in the 2D pixel space and lack explicit…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Yunpeng Bai , Shaoheng Fang , Chaohui Yu , Fan Wang , Qixing Huang

Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation process is an important goal moving forward and recent…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Zhengfei Kuang , Shengqu Cai , Hao He , Yinghao Xu , Hongsheng Li , Leonidas Guibas , Gordon Wetzstein

Recent advances in diffusion models have revolutionized video generation, offering superior temporal consistency and visual quality compared to traditional generative adversarial networks-based approaches. While this emerging field shows…

Computer Vision and Pattern Recognition · Computer Science 2026-02-11 Yimu Wang , Xuye Liu , Wei Pang , Li Ma , Shuai Yuan , Paul Debevec , Ning Yu

We address the problem of generating long-horizon videos for robotic manipulation tasks. Text-to-video diffusion models have made significant progress in photorealism, language understanding, and motion generation but struggle with…

Computer Vision and Pattern Recognition · Computer Science 2025-06-30 Liudi Yang , Yang Bai , George Eskandar , Fengyi Shen , Mohammad Altillawi , Dong Chen , Soumajit Majumder , Ziyuan Liu , Gitta Kutyniok , Abhinav Valada

Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap can be observed between these two fields, e.g., scalable 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Sibo Wu , Congrong Xu , Binbin Huang , Andreas Geiger , Anpei Chen

We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal…

Computer Vision and Pattern Recognition · Computer Science 2023-05-04 Nikita Karaev , Ignacio Rocco , Benjamin Graham , Natalia Neverova , Andrea Vedaldi , Christian Rupprecht

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

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