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Despite the typical inversion-then-editing paradigm using text-to-image (T2I) models has demonstrated promising results, directly extending it to text-to-video (T2V) models still suffers severe artifacts such as color flickering and content…

Computer Vision and Pattern Recognition · Computer Science 2025-03-20 Yukun Wang , Longguang Wang , Zhiyuan Ma , Qibin Hu , Kai Xu , Yulan Guo

Advancements in 3D scene reconstruction have transformed 2D images from the real world into 3D models, producing realistic 3D results from hundreds of input photos. Despite great success in dense-view reconstruction scenarios, rendering a…

Computer Vision and Pattern Recognition · Computer Science 2025-06-26 Fangfu Liu , Wenqiang Sun , Hanyang Wang , Yikai Wang , Haowen Sun , Junliang Ye , Jun Zhang , Yueqi Duan

Recently, diffusion models like StableDiffusion have achieved impressive image generation results. However, the generation process of such diffusion models is uncontrollable, which makes it hard to generate videos with continuous and…

Computer Vision and Pattern Recognition · Computer Science 2023-08-04 Zhihao Hu , Dong Xu

In this report, we present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task. We found that high-fidelity and temporally coherent video-to-video translation can be achieved by explicitly…

Computer Vision and Pattern Recognition · Computer Science 2023-08-29 Jun Hao Liew , Hanshu Yan , Jianfeng Zhang , Zhongcong Xu , Jiashi Feng

Temporal consistency is essential for video editing applications. Existing work on layered representation of videos allows propagating edits consistently to each frame. These methods, however, can only edit object appearance rather than…

Computer Vision and Pattern Recognition · Computer Science 2023-01-31 Yao-Chih Lee , Ji-Ze Genevieve Jang , Yi-Ting Chen , Elizabeth Qiu , Jia-Bin Huang

Recent advances in generative video models have enabled the creation of high-quality videos based on natural language prompts. However, these models frequently lack fine-grained temporal control, meaning they do not allow users to specify…

Computer Vision and Pattern Recognition · Computer Science 2026-04-02 Shira Schiber , Ofir Lindenbaum , Idan Schwartz

Video inpainting aims to fill spatio-temporal holes with plausible content in a video. Despite tremendous progress of deep neural networks for image inpainting, it is challenging to extend these methods to the video domain due to the…

Computer Vision and Pattern Recognition · Computer Science 2019-05-07 Dahun Kim , Sanghyun Woo , Joon-Young Lee , In So Kweon

Real-world instructional videos are long, noisy, and often contain extended background segments, repeated actions, and execution variability that do not correspond to meaningful procedural steps. We propose **REMAP**, an unsupervised…

Computer Vision and Pattern Recognition · Computer Science 2026-05-08 Soumyadeep Chandra , Kaushik Roy

Precise and flexible image editing remains a fundamental challenge in computer vision. Based on the modified areas, most editing methods can be divided into two main types: global editing and local editing. In this paper, we choose the two…

Computer Vision and Pattern Recognition · Computer Science 2025-02-27 Ziqi Jiang , Zhen Wang , Long Chen

Video (camera) trajectory editing aims to synthesize new videos that follow user-defined camera paths while preserving scene content and plausibly inpainting previously unseen regions, upgrading amateur footage into professionally styled…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Zhihao Shi , Kejia Yin , Weilin Wan , Yuhongze Zhou , Yuanhao Yu , Xinxin Zuo , Qiang Sun , Juwei Lu

Recent diffusion models have achieved remarkable success in image relighting, and this success has quickly been extended to video relighting. However, existing methods offer limited explicit control over illumination in the relighted…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Yizuo Peng , Xuelin Chen , Kai Zhang , Xiaodong Cun

We address the problem of data augmentation for video action recognition. Standard augmentation strategies in video are hand-designed and sample the space of possible augmented data points either at random, without knowing which augmented…

Computer Vision and Pattern Recognition · Computer Science 2022-07-26 Shreyank N Gowda , Marcus Rohrbach , Frank Keller , Laura Sevilla-Lara

Video chroma-lux editing, which aims to modify illumination and color while preserving structural and temporal fidelity, remains a significant challenge. Existing methods typically rely on expensive supervised training with synthetic paired…

Computer Vision and Pattern Recognition · Computer Science 2026-04-16 Yifan Li , Pei Cheng , Bin Fu , Shuai Yang , Jiaying Liu

Accurately preserving motion while editing a subject remains a core challenge in video editing tasks. Existing methods often face a trade-off between edit and motion fidelity, as they rely on motion representations that are either…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Yeji Song , Jaehyun Lee , Mijin Koo , JunHoo Lee , Nojun Kwak

This study introduces an efficient and effective method, MeDM, that utilizes pre-trained image Diffusion Models for video-to-video translation with consistent temporal flow. The proposed framework can render videos from scene position…

Computer Vision and Pattern Recognition · Computer Science 2023-12-21 Ernie Chu , Tzuhsuan Huang , Shuo-Yen Lin , Jun-Cheng Chen

Instruction-based video editing aims to modify an input video according to a natural-language instruction while preserving content fidelity and temporal coherence. However, existing diffusion-based approaches are often trained on paired…

Computer Vision and Pattern Recognition · Computer Science 2025-12-19 Xiaoyan Cong , Haotian Yang , Angtian Wang , Yizhi Wang , Yiding Yang , Canyu Zhang , Chongyang Ma

Video inbetweening creates smooth and natural transitions between two image frames, making it an indispensable tool for video editing and long-form video synthesis. Existing works in this domain are unable to generate large, complex, or…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Maham Tanveer , Yang Zhou , Simon Niklaus , Ali Mahdavi Amiri , Hao Zhang , Krishna Kumar Singh , Nanxuan Zhao

Technological advances in sensors have paved the way for digital cameras to become increasingly ubiquitous, which, in turn, led to the popularity of the self-recording culture. As a result, the amount of visual data on the Internet is…

Computer Vision and Pattern Recognition · Computer Science 2020-09-24 Michel Melo Silva , Washington Luis Souza Ramos , Mario Fernando Montenegro Campos , Erickson Rangel Nascimento

In this paper, we propose a novel diffusion-based multi-condition controllable framework for video head swapping, which seamlessly transplant a human head from a static image into a dynamic video, while preserving the original body and…

Computer Vision and Pattern Recognition · Computer Science 2025-06-23 Chaonan Ji , Jinwei Qi , Peng Zhang , Bang Zhang , Liefeng Bo

Atmospheric turbulence severely degrades video quality by introducing distortions such as geometric warping, blur, and temporal flickering, posing significant challenges to both visual clarity and temporal consistency. Current…

Computer Vision and Pattern Recognition · Computer Science 2026-05-21 Zhiming Liu , Zhicheng Zou , Nantheera Anantrasirichai
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