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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

We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content. Our approach is based on a factor graph representation where each…

Computer Vision and Pattern Recognition · Computer Science 2023-03-31 Qinsheng Zhang , Jiaming Song , Xun Huang , Yongxin Chen , Ming-Yu Liu

Generating consistent multiple views for 3D reconstruction tasks is still a challenge to existing image-to-3D diffusion models. Generally, incorporating 3D representations into diffusion model decrease the model's speed as well as…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Emmanuelle Bourigault , Pauline Bourigault

We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compositing pipeline: (i) segmenting and converting visual inputs…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Jiacheng Chen , Ramin Mehran , Xuhui Jia , Saining Xie , Sanghyun Woo

Automatic font generation is an imitation task, which aims to create a font library that mimics the style of reference images while preserving the content from source images. Although existing font generation methods have achieved…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Zhenhua Yang , Dezhi Peng , Yuxin Kong , Yuyi Zhang , Cong Yao , Lianwen Jin

Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual prompt. However, the internal representations learned by these models remain an enigma. In this work, we…

Computer Vision and Pattern Recognition · Computer Science 2023-10-06 Hila Chefer , Oran Lang , Mor Geva , Volodymyr Polosukhin , Assaf Shocher , Michal Irani , Inbar Mosseri , Lior Wolf

Generating images from text involving complex and novel object arrangements remains a significant challenge for current text-to-image (T2I) models. Although prior layout-based methods improve object arrangements using spatial constraints…

Computer Vision and Pattern Recognition · Computer Science 2025-06-02 Zeeshan Khan , Shizhe Chen , Cordelia Schmid

Generative object compositing emerges as a promising new avenue for compositional image editing. However, the requirement of object identity preservation poses a significant challenge, limiting practical usage of most existing methods. In…

Computer Vision and Pattern Recognition · Computer Science 2024-03-19 Yizhi Song , Zhifei Zhang , Zhe Lin , Scott Cohen , Brian Price , Jianming Zhang , Soo Ye Kim , He Zhang , Wei Xiong , Daniel Aliaga

Generating high-quality novel views of a scene from a single image requires maintaining structural coherence across different views, referred to as view consistency. While diffusion models have driven advancements in novel view synthesis,…

Computer Vision and Pattern Recognition · Computer Science 2025-08-07 Jiwoo Park , Tae Eun Choi , Youngjun Jun , Seong Jae Hwang

Viewpoint missing of objects is common in scene reconstruction, as camera paths typically prioritize capturing the overall scene structure rather than individual objects. This makes it highly challenging to achieve high-fidelity…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Ziwei Chen , Ziling Liu , Zitong Huang , Mingqi Gao , Feng Zheng

Diffusion models have enabled high-quality, conditional image editing capabilities. We propose to expand their arsenal, and demonstrate that off-the-shelf diffusion models can be used for a wide range of cross-domain compositing tasks.…

Computer Vision and Pattern Recognition · Computer Science 2023-05-26 Roy Hachnochi , Mingrui Zhao , Nadav Orzech , Rinon Gal , Ali Mahdavi-Amiri , Daniel Cohen-Or , Amit Haim Bermano

Diffusion-based 3D generation has made remarkable progress in recent years. However, existing 3D generative models often produce overly dense and unstructured meshes, which stand in stark contrast to the compact, structured, and…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Yuan Li , Cheng Lin , Yuan Liu , Xiaoxiao Long , Chenxu Zhang , Ningna Wang , Xin Li , Wenping Wang , Xiaohu Guo

Diffusion models have shown great promise in synthesizing visually appealing images. However, it remains challenging to condition the synthesis at a fine-grained level, for instance, synthesizing image pixels following some generic color…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Ka Chun Shum , Binh-Son Hua , Duc Thanh Nguyen , Sai-Kit Yeung

Text-to-image diffusion models have recently taken center stage as pivotal tools in promoting visual creativity across an array of domains such as comic book artistry, children's literature, game development, and web design. These models…

Computer Vision and Pattern Recognition · Computer Science 2024-06-06 Kiymet Akdemir , Pinar Yanardag

The customization of text-to-image models has seen significant advancements, yet generating multiple personalized concepts remains a challenging task. Current methods struggle with attribute leakage and layout confusion when handling…

Computer Vision and Pattern Recognition · Computer Science 2024-09-10 Zebin Yao , Fangxiang Feng , Ruifan Li , Xiaojie Wang

Reconstructing a renderable 3D model from images is a useful but challenging task. Recent feedforward 3D reconstruction methods have demonstrated remarkable success in efficiently recovering geometry, but still cannot accurately model the…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Zekai Gu , Shuoxuan Feng , Yansong Wang , Hanzhuo Huang , Zhongshuo Du , Chengfeng Zhao , Chengwei Ren , Peng Wang , Yuan Liu

Recent advancements in image generation have made significant progress, yet existing models present limitations in perceiving and generating an arbitrary number of interrelated images within a broad context. This limitation becomes…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Ying Shen , Yizhe Zhang , Shuangfei Zhai , Lifu Huang , Joshua M. Susskind , Jiatao Gu

Text-to-image diffusion models enable high-quality image generation but are computationally expensive. While prior work optimizes per-inference efficiency, we explore an orthogonal approach: reducing redundancy across correlated prompts.…

Computer Vision and Pattern Recognition · Computer Science 2025-08-29 Dale Decatur , Thibault Groueix , Wang Yifan , Rana Hanocka , Vladimir Kim , Matheus Gadelha

Text-to-image diffusion models have demonstrated tremendous success in synthesizing visually stunning images given textual instructions. Despite remarkable progress in creating high-fidelity visuals, text-to-image models can still struggle…

Computer Vision and Pattern Recognition · Computer Science 2025-12-15 Taewook Kim , Ze Wang , Zhengyuan Yang , Jiang Wang , Lijuan Wang , Zicheng Liu , Qiang Qiu

Multistep instructions, such as recipes and how-to guides, greatly benefit from visual aids, such as a series of images that accompany the instruction steps. While Large Language Models (LLMs) have become adept at generating coherent…

Computer Vision and Pattern Recognition · Computer Science 2024-05-17 João Bordalo , Vasco Ramos , Rodrigo Valério , Diogo Glória-Silva , Yonatan Bitton , Michal Yarom , Idan Szpektor , Joao Magalhaes