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Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts. However, such solutions typically incur heavy…

Computer Vision and Pattern Recognition · Computer Science 2024-09-02 Kumara Kahatapitiya , Adil Karjauv , Davide Abati , Fatih Porikli , Yuki M. Asano , Amirhossein Habibian

Restoring low-resolution text images presents a significant challenge, as it requires maintaining both the fidelity and stylistic realism of the text in restored images. Existing text image restoration methods often fall short in hard…

Computer Vision and Pattern Recognition · Computer Science 2025-06-17 Chenglu Pan , Xiaogang Xu , Ganggui Ding , Yunke Zhang , Wenbo Li , Jiarong Xu , Qingbiao Wu

The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of…

Machine Learning · Computer Science 2025-01-09 Stanley H. Chan

Diffusion models have made significant strides in image generation, mastering tasks such as unconditional image synthesis, text-image translation, and image-to-image conversions. However, their capability falls short in the realm of video…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Gaurav Shrivastava , Abhinav Shrivastava

State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Andrey Voynov , Amir Hertz , Moab Arar , Shlomi Fruchter , Daniel Cohen-Or

Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Levon Khachatryan , Andranik Movsisyan , Vahram Tadevosyan , Roberto Henschel , Zhangyang Wang , Shant Navasardyan , Humphrey Shi

Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose…

Computer Vision and Pattern Recognition · Computer Science 2023-11-21 Chong Mou , Xintao Wang , Jiechong Song , Ying Shan , Jian Zhang

Text-based diffusion models have exhibited remarkable success in generation and editing, showing great promise for enhancing visual content with their generative prior. However, applying these models to video super-resolution remains…

Computer Vision and Pattern Recognition · Computer Science 2023-12-12 Shangchen Zhou , Peiqing Yang , Jianyi Wang , Yihang Luo , Chen Change Loy

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

We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from the original image and preserve the identity of its parts.…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Hadi Alzayer , Zhihao Xia , Xuaner Zhang , Eli Shechtman , Jia-Bin Huang , Michael Gharbi

Recently, the multimedia community has witnessed the rise of diffusion models trained on large-scale multi-modal data for visual content creation, particularly in the field of text-to-image generation. In this paper, we propose a new task…

Computer Vision and Pattern Recognition · Computer Science 2023-11-10 Jingwen Chen , Yingwei Pan , Ting Yao , Tao Mei

Recently, diffusion-based generative models have achieved remarkable success for image generation and edition. However, existing diffusion-based video editing approaches lack the ability to offer precise control over generated content that…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Paul Couairon , Clément Rambour , Jean-Emmanuel Haugeard , Nicolas Thome

Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion models primarily focus on incorporating text-visual…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Ling Yang , Zhilong Zhang , Zhaochen Yu , Jingwei Liu , Minkai Xu , Stefano Ermon , Bin Cui

With the rise of large, publicly-available text-to-image diffusion models, text-guided real image editing has garnered much research attention recently. Existing methods tend to either rely on some form of per-instance or per-task…

Computer Vision and Pattern Recognition · Computer Science 2022-11-16 Adham Elarabawy , Harish Kamath , Samuel Denton

Diffusion models have demonstrated remarkable capabilities in text-to-image and text-to-video generation, opening up possibilities for video editing based on textual input. However, the computational cost associated with sequential sampling…

Computer Vision and Pattern Recognition · Computer Science 2024-11-20 Youyuan Zhang , Xuan Ju , James J. Clark

Deepfake images are fast becoming a serious concern due to their realism. Diffusion models have recently demonstrated highly realistic visual content generation, which makes them an excellent potential tool for Deepfake generation. To curb…

Computer Vision and Pattern Recognition · Computer Science 2023-09-27 Yunzhuo Chen , Nur Al Hasan Haldar , Naveed Akhtar , Ajmal Mian

Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these…

Computer Vision and Pattern Recognition · Computer Science 2024-01-18 Haoxin Chen , Yong Zhang , Xiaodong Cun , Menghan Xia , Xintao Wang , Chao Weng , Ying Shan

Diffusion models have shown remarkable capabilities in generating high quality and creative images conditioned on text. An interesting application of such models is structure preserving text guided image editing. Existing approaches rely on…

Computer Vision and Pattern Recognition · Computer Science 2023-03-22 Hareesh Ravi , Sachin Kelkar , Midhun Harikumar , Ajinkya Kale

The text-driven image and video diffusion models have achieved unprecedented success in generating realistic and diverse content. Recently, the editing and variation of existing images and videos in diffusion-based generative models have…

Computer Vision and Pattern Recognition · Computer Science 2024-02-20 Yuyang Zhao , Enze Xie , Lanqing Hong , Zhenguo Li , Gim Hee Lee

Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently…

Computer Vision and Pattern Recognition · Computer Science 2025-03-21 Noam Rotstein , Gal Yona , Daniel Silver , Roy Velich , David Bensaïd , Ron Kimmel