KISSColor: Kinetic and Intuitive Stroke Stretching for Vector Drawing Colorization
Hand-drawn vector sketches often contain implied lines, imprecise intersections, and unintended gaps, making it challenging to identify closed regions for colorization. These challenges become more pronounced as the number of strokes increases. In this paper, we present KISSColor, a novel method for inferring users' intended closed regions. Specifically, we propose intuitive stroke stretching by extending open strokes along tangent isolines of winding-number fields, which provably form geometrically aligned closed regions. Extending all open strokes can lead to overly fragmented regions due to redundant intersections. While a Mixed Integer Programming (MIP) formulation helps reduce redundancy, it is computationally expensive. To improve efficiency, we introduce kinetic stroke stretching, which grows all strokes simultaneously and prioritizes early intersections using a kinetic data structure. This approach preserves stylistic ambiguity for lines requiring long extensions. Based on the growth results, redundant regions are suppressed to minimize fragmentation. We conduct extensive experiments demonstrating the effectiveness of KISSColor, which generates more intuitive partitions, especially for imprecise sketches (see teaser figure). Our code and data will be released upon publication.
Authors
- Hongxu Xin (ORCID: https://orcid.org/0009-0008-2532-7428)
- Taku Komura (ORCID: https://orcid.org/0000-0002-2729-5860)
- Yuan Liu (ORCID: https://orcid.org/0000-0003-2933-5667)
- Shuangmin Chen (ORCID: https://orcid.org/0000-0002-0835-3316)
- Zhiyang Dou (ORCID: https://orcid.org/0000-0003-0186-8269)
- Shiqing Xin (ORCID: https://orcid.org/0000-0001-8452-8723)
- Changhe Tu (ORCID: https://orcid.org/0000-0002-1231-3392)
- Wenping Wang (ORCID: https://orcid.org/0000-0002-2284-3952)
- Rui Xu (ORCID: https://orcid.org/0000-0001-8273-1808)
- Yujia Dong (ORCID: https://orcid.org/0009-0004-7121-7511)
Institutions
- Qingdao University (CN)
- Qingdao University of Science and Technology (CN)
- Qingdao Agricultural University (CN)
- Shandong University (CN)
- Hong Kong Opto-Mechatronics Industries Association (CN)
- Yingkou Institute of Technology (CN)
- University of Hong Kong (HK)
- Texas A&M University (US)
Publication Details
- Journal
- ACM Transactions on Graphics
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1145/3763307
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 0.00