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Paper Abstract and Keywords
Presentation 2024-02-20 11:00
Image Generation Modification with Diffusion Model through Sketch Guidance
Sandra Zhang Ding, Jiafeng Mao, Kiyoharu Aizawa (UTokyo) ITS2023-64 IE2023-53
Abstract (in Japanese) (See Japanese page) 
(in English) Large-scale image generation models have demonstrated their remarkable ability to generate diverse, high-quality images. However, harnessing their potential for real-world content creation, particularly in providing users with fine-grained control over image generation, remains a challenge. In this paper, we propose a novel pipeline that extends existing text-to-image generation models to accept sketch as an additional input. This approach not only retains the original capabilities of the models but also enriches them by allowing users to input structural guidance through sketches. By extracting core features from the sketch, users can create a new image that has a similar layout and structure to the input image. Our approach leverages the power of a pre-trained text-to-image diffusion model, enabling the generation of an image that aligns with both the layout structure of the sketch and the accompanying text prompt. This results in a straightforward yet effective method, where the sketch's structural information is directly and efficiently incorporated into the image generation process without additional training or calibration of foundational models.
Keyword (in Japanese) (See Japanese page) 
(in English) sketch-to-image / text-to-image / diffusion-model / fine-grained control / image generation / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 381, IE2023-53, pp. 95-99, Feb. 2024.
Paper # IE2023-53 
Date of Issue 2024-02-12 (ITS, IE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF ITS2023-64 IE2023-53

Conference Information
Committee ITS IE ITE-MMS ITE-ME ITE-AIT  
Conference Date 2024-02-19 - 2024-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To IE 
Conference Code 2024-02-ITS-IE-MMS-ME-AIT 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Image Generation Modification with Diffusion Model through Sketch Guidance 
Sub Title (in English)  
Keyword(1) sketch-to-image  
Keyword(2) text-to-image  
Keyword(3) diffusion-model  
Keyword(4) fine-grained control  
Keyword(5) image generation  
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1st Author's Name Sandra Zhang Ding  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Jiafeng Mao  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Kiyoharu Aizawa  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2024-02-20 11:00:00 
Presentation Time 15 minutes 
Registration for IE 
Paper # ITS2023-64, IE2023-53 
Volume (vol) vol.123 
Number (no) no.380(ITS), no.381(IE) 
Page pp.95-99 
#Pages 5 
Date of Issue 2024-02-12 (ITS, IE) 

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