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Paper Abstract and Keywords
Presentation 2026-05-13 14:50
Object generation method faithful to mask shape using semantic masks
Kei Takamatsu, Guoqing Hao, Kazuhiko Sumi (Aoyama Gakuin Univ.) PRMU2026-6
Abstract (in Japanese) (See Japanese page) 
(in English) Recent text-guided image generation models can synthesize high-quality images, but it is still difficult to generate an object of an intended category faithfully within a user-specified region and shape. In mask-based inpainting, generated objects may overflow from the mask, miss some parts, or have unnatural boundaries. In addition, variations in textual expressions between training and inference can make it difficult to isolate the effect of semantic masks. In this study, we investigate a method that takes a background image and a semantic mask
as conditions and generates an object in the specified region by using ControlNet. A dataset of ten categories was constructed from the MSCOCO segmentation annotations, and the text prompt was fixed to the category name to reduce unnecessary variation in textconditions. Experimental results show that the proposed method improves FID and IoU over a retrained SD1.5 Inpainting baseline, indicating better shape consistency with the mask. On the other hand, failures remain for complex backgrounds and thin masks, and preliminary experiments on easy mask generation reveal that balancing category consistency and faithful shape preservation is still an open problem.
Keyword (in Japanese) (See Japanese page) 
(in English) image generation / inpainting / ControlNet / semantic mask / diffusion model / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 16, PRMU2026-6, pp. 32-37, May 2026.
Paper # PRMU2026-6 
Date of Issue 2026-05-06 (PRMU) 
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 PRMU2026-6

Conference Information
Committee PRMU IPSJ-CVIM  
Conference Date 2026-05-13 - 2026-05-14 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2026-05-PRMU-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Object generation method faithful to mask shape using semantic masks 
Sub Title (in English)  
Keyword(1) image generation  
Keyword(2) inpainting  
Keyword(3) ControlNet  
Keyword(4) semantic mask  
Keyword(5) diffusion model  
Keyword(6)  
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1st Author's Name Kei Takamatsu  
1st Author's Affiliation Aoyama Gakuin University (Aoyama Gakuin Univ.)
2nd Author's Name Guoqing Hao  
2nd Author's Affiliation Aoyama Gakuin University (Aoyama Gakuin Univ.)
3rd Author's Name Kazuhiko Sumi  
3rd Author's Affiliation Aoyama Gakuin University (Aoyama Gakuin Univ.)
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Speaker Author-1 
Date Time 2026-05-13 14:50:00 
Presentation Time 1 minutes 
Registration for PRMU 
Paper # PRMU2026-6 
Volume (vol) vol.126 
Number (no) no.16 
Page pp.32-37 
#Pages 6 
Date of Issue 2026-05-06 (PRMU) 

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