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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

With the rapid advancement of intelligent transportation systems, text-driven image generation and editing techniques have demonstrated significant potential in providing rich, controllable visual scene data for applications such as traffic…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Feng Lv , Haoxuan Feng , Zilu Zhang , Chunlong Xia , Yanfeng Li

Video editing has garnered increasing attention alongside the rapid progress of diffusion-based video generation models. As part of these advancements, there is a growing demand for more accessible and controllable forms of video editing,…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Min-Jung Kim , Dongjin Kim , Seokju Yun , Jaegul Choo

Diffusion models (DMs) can generate realistic images with text guidance using large-scale datasets. However, they demonstrate limited controllability in the output space of the generated images. We propose a novel learning method for…

Computer Vision and Pattern Recognition · Computer Science 2023-05-11 Rumeysa Bodur , Erhan Gundogdu , Binod Bhattarai , Tae-Kyun Kim , Michael Donoser , Loris Bazzani

Research in vision-language models has seen rapid developments off-late, enabling natural language-based interfaces for image generation and manipulation. Many existing text guided manipulation techniques are restricted to specific classes…

Computer Vision and Pattern Recognition · Computer Science 2024-05-07 Paramanand Chandramouli , Kanchana Vaishnavi Gandikota

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

Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is still challenging to directly apply these models for editing real images for two reasons. First,…

Computer Vision and Pattern Recognition · Computer Science 2023-02-07 Gaurav Parmar , Krishna Kumar Singh , Richard Zhang , Yijun Li , Jingwan Lu , Jun-Yan Zhu

Image diffusion models, trained on massive image collections, have emerged as the most versatile image generator model in terms of quality and diversity. They support inverting real images and conditional (e.g., text) generation, making…

Computer Vision and Pattern Recognition · Computer Science 2023-03-23 Duygu Ceylan , Chun-Hao Paul Huang , Niloy J. Mitra

Large-scale text-to-image diffusion models achieve unprecedented success in image generation and editing. However, how to extend such success to video editing is unclear. Recent initial attempts at video editing require significant…

Computer Vision and Pattern Recognition · Computer Science 2024-01-05 Wen Wang , Yan Jiang , Kangyang Xie , Zide Liu , Hao Chen , Yue Cao , Xinlong Wang , Chunhua Shen

This paper proposes a novel and physically interpretable method for face editing based on arbitrary text prompts. Different from previous GAN-inversion-based face editing methods that manipulate the latent space of GANs, or diffusion-based…

Computer Vision and Pattern Recognition · Computer Science 2023-08-14 Yapeng Meng , Songru Yang , Xu Hu , Rui Zhao , Lincheng Li , Zhenwei Shi , Zhengxia Zou

Image and shape editing are ubiquitous among digital artworks. Graphics algorithms facilitate artists and designers to achieve desired editing intents without going through manually tedious retouching. In the recent advance of machine…

Graphics · Computer Science 2023-04-20 Cheng-Kang Ted Chao , Yotam Gingold

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

Text-guided image generation aimed to generate desired images conditioned on given texts, while text-guided image manipulation refers to semantically edit parts of a given image based on specified texts. For these two similar tasks, the key…

Computer Vision and Pattern Recognition · Computer Science 2023-09-22 Xiaozhou You , Jian Zhang

Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining…

Computer Vision and Pattern Recognition · Computer Science 2024-04-09 Pengzhi Li , QInxuan Huang , Yikang Ding , Zhiheng Li

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part…

Computer Vision and Pattern Recognition · Computer Science 2021-03-02 Aditya Ramesh , Mikhail Pavlov , Gabriel Goh , Scott Gray , Chelsea Voss , Alec Radford , Mark Chen , Ilya Sutskever

Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains. While these methods have incrementally improved the generated image fidelity and text relevancy, several pivotal…

Computer Vision and Pattern Recognition · Computer Science 2022-03-25 Oran Gafni , Adam Polyak , Oron Ashual , Shelly Sheynin , Devi Parikh , Yaniv Taigman

Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., "a woman is drinking water."). Existing TI2V frameworks often require…

Computer Vision and Pattern Recognition · Computer Science 2024-04-26 Haomiao Ni , Bernhard Egger , Suhas Lohit , Anoop Cherian , Ye Wang , Toshiaki Koike-Akino , Sharon X. Huang , Tim K. Marks

High-quality HDRIs(High Dynamic Range Images), typically HDR panoramas, are one of the most popular ways to create photorealistic lighting and 360-degree reflections of 3D scenes in graphics. Given the difficulty of capturing HDRIs, a…

Computer Vision and Pattern Recognition · Computer Science 2023-04-17 Zhaoxi Chen , Guangcong Wang , Ziwei Liu

Language has emerged as a natural interface for image editing. In this paper, we introduce a method for region-based image editing driven by textual prompts, without the need for user-provided masks or sketches. Specifically, our approach…

Computer Vision and Pattern Recognition · Computer Science 2024-04-04 Yuanze Lin , Yi-Wen Chen , Yi-Hsuan Tsai , Lu Jiang , Ming-Hsuan Yang

Scene graphs offer a structured, hierarchical representation of images, with nodes and edges symbolizing objects and the relationships among them. It can serve as a natural interface for image editing, dramatically improving precision and…

Computer Vision and Pattern Recognition · Computer Science 2024-10-16 Zhiyuan Zhang , DongDong Chen , Jing Liao
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