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In this work, we address a challenge in video inpainting: reconstructing occluded regions in dynamic, real-world scenarios. Motivated by the need for continuous human motion monitoring in healthcare settings, where facial features are…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Zheyan Zhang , Diego Klabjan , Renee CB Manworren

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we…

Computer Vision and Pattern Recognition · Computer Science 2023-11-21 Michal Geyer , Omer Bar-Tal , Shai Bagon , Tali Dekel

Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable…

Computer Vision and Pattern Recognition · Computer Science 2023-09-19 Shuai Yang , Yifan Zhou , Ziwei Liu , Chen Change Loy

The depth-of-field (DoF) effect, which introduces aesthetically pleasing blur, enhances photographic quality but is fixed and difficult to modify once the image has been created. This becomes problematic when the applied blur is…

Computer Vision and Pattern Recognition · Computer Science 2025-10-01 Yiyang Wang , Xi Chen , Xiaogang Xu , Yu Liu , Hengshuang Zhao

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

Recent advances in diffusion models have successfully enabled text-guided image inpainting. While it seems straightforward to extend such editing capability into the video domain, there have been fewer works regarding text-guided video…

Computer Vision and Pattern Recognition · Computer Science 2024-04-02 Zhixing Zhang , Bichen Wu , Xiaoyan Wang , Yaqiao Luo , Luxin Zhang , Yinan Zhao , Peter Vajda , Dimitris Metaxas , Licheng Yu

Image generation and editing have seen a great deal of advancements with the rise of large-scale diffusion models that allow user control of different modalities such as text, mask, depth maps, etc. However, controlled editing of videos…

Computer Vision and Pattern Recognition · Computer Science 2024-06-04 AmirHossein Zamani , Amir G. Aghdam , Tiberiu Popa , Eugene Belilovsky

The remarkable generative capabilities of diffusion models have motivated extensive research in both image and video editing. Compared to video editing which faces additional challenges in the time dimension, image editing has witnessed the…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Wenqi Ouyang , Yi Dong , Lei Yang , Jianlou Si , Xingang Pan

Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background. However, it is challenging for existing bokeh rendering methods to simulate realistic…

Computer Vision and Pattern Recognition · Computer Science 2022-07-19 Juewen Peng , Jianming Zhang , Xianrui Luo , Hao Lu , Ke Xian , Zhiguo Cao

Volumetric video relighting is essential for bringing captured performances into virtual worlds, but current approaches struggle to deliver temporally stable, production-ready results. Diffusion-based intrinsic decomposition methods show…

Computer Vision and Pattern Recognition · Computer Science 2026-01-23 Elisabeth Jüttner , Janelle Pfeifer , Leona Krath , Stefan Korfhage , Hannah Dröge , Matthias B. Hullin , Markus Plack

Video face restoration aims to enhance degraded face videos into high-quality results with realistic facial details, stable identity, and temporal coherence. Recent diffusion-based methods have brought strong generative priors to…

Computer Vision and Pattern Recognition · Computer Science 2026-04-17 Zheng Chen , Bowen Chai , Rongjun Gao , Mingtao Nie , Xi Li , Bingnan Duan , Jianping Fang , Xiaohong Liu , Linghe Kong , Yulun Zhang

Event-based video reconstruction has garnered increasing attention due to its advantages, such as high dynamic range and rapid motion capture capabilities. However, current methods often prioritize the extraction of temporal information…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Lin Zhu , Yunlong Zheng , Yijun Zhang , Xiao Wang , Lizhi Wang , Hua Huang

Video inpainting is the task of filling a region in a video in a visually convincing manner. It is very challenging due to the high dimensionality of the data and the temporal consistency required for obtaining convincing results. Recently,…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Nicolas Cherel , Andrés Almansa , Yann Gousseau , Alasdair Newson

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

The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, there is a growing interest in exploring the application of…

Computer Vision and Pattern Recognition · Computer Science 2023-06-21 Ariel Lapid , Idan Achituve , Lior Bracha , Ethan Fetaya

Emerging video diffusion models achieve high visual fidelity but fundamentally couple scene dynamics with camera motion, limiting their ability to provide precise spatial and temporal control. We introduce a 4D-controllable video diffusion…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Yiming Wang , Qihang Zhang , Shengqu Cai , Tong Wu , Jan Ackermann , Zhengfei Kuang , Yang Zheng , Frano Rajič , Siyu Tang , 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

Text-driven video generation witnesses rapid progress. However, merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users' intents, especially for customized content creation. In this…

Computer Vision and Pattern Recognition · Computer Science 2023-12-04 Yuming Jiang , Tianxing Wu , Shuai Yang , Chenyang Si , Dahua Lin , Yu Qiao , Chen Change Loy , Ziwei Liu

The generation of sounding videos has seen significant advancements with the advent of diffusion models. However, existing methods often lack the fine-grained control needed to generate viewpoint-specific content from larger, immersive…

We introduce InVi, an approach for inserting or replacing objects within videos (referred to as inpainting) using off-the-shelf, text-to-image latent diffusion models. InVi targets controlled manipulation of objects and blending them…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Nirat Saini , Navaneeth Bodla , Ashish Shrivastava , Avinash Ravichandran , Xiao Zhang , Abhinav Shrivastava , Bharat Singh