Enabling Region-Specific Control via Lassos in Point-Based Colorization

Point-based interactive colorization techniques allow users to effortlessly colorize grayscale images using user-provided color hints. However, point-based methods often face challenges when different colors are given to semantically similar areas, leading to color intermingling and unsatisfactory results—an issue we refer to as color collapse. The fundamental cause of color collapse is the inadequacy of points for defining the boundaries for each color. To mitigate color collapse, we introduce a lasso tool that can control the scope of each color hint. Additionally, we design a framework that leverages the user-provided lassos to localize the attention masks. The experimental results show that using a single lasso is as effective as applying 4.18 individual color hints and can achieve the desired outcomes in 30% less time than using points alone.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i5.32479
Primary Topic
Advanced Vision and Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Enabling Region-Specific Control via Lassos in Point-Based Colorization

Jooyeol Yun, Jaegul Choo, Sanghyeon Lee
Proceedings of the AAAI Conference on Artificial Intelligence
Advanced Vision and Imaging
article

Enabling Region-Specific Control via Lassos in Point-Based Colorization

Jooyeol Yun, Jaegul Choo, Sanghyeon Lee
article en

Abstract

Point-based interactive colorization techniques allow users to effortlessly colorize grayscale images using user-provided color hints. However, point-based methods often face challenges when different colors are given to semantically similar areas, leading to color intermingling and unsatisfactory results—an issue we refer to as color collapse. The fundamental cause of color collapse is the inadequacy of points for defining the boundaries for each color. To mitigate color collapse, we introduce a lasso tool that can control the scope of each color hint. Additionally, we design a framework that leverages the user-provided lassos to localize the attention masks. The experimental results show that using a single lasso is as effective as applying 4.18 individual color hints and can achieve the desired outcomes in 30% less time than using points alone.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(5)
Korea Advanced Institute of Science and Technology (KR)
Korea Advanced Institute of Science and Technology, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
Openalex Percentile: Top 75%
Advanced Vision and Imaging
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