Sketch to Stylized-Image: A Two-Stage Approach for Artistic Image Generation

Transforming sketches into artistic images is an essential task, especially for individuals without artistic expertise. Recent style transfer models struggle with applying styles to sketch images due to the absence of color information in the input image. To address this limitation, we propose a novel approach that leverages existing pre-trained models. Our method first colors the sketches using a well-performing pre-trained condition-based image generation model, and then applies style transfer to achieve the desired artistic effect. Experimental results demonstrate that our method effectively generates stylized sketch images with high quality, without the overhead of additional training.

Authors

Institutions

Publication Details

Published
2025-08-18
DOI
https://doi.org/10.1109/icip55913.2025.11084678
Primary Topic
Aesthetic Perception and Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sketch to Stylized-Image: A Two-Stage Approach for Artistic Image Generation

Jong Seok Lee, Junha Park, Ji-Yoon Kim
Aesthetic Perception and Analysis
article

Sketch to Stylized-Image: A Two-Stage Approach for Artistic Image Generation

Jong Seok Lee, Junha Park, Ji-Yoon Kim
article en

Abstract

Transforming sketches into artistic images is an essential task, especially for individuals without artistic expertise. Recent style transfer models struggle with applying styles to sketch images due to the absence of color information in the input image. To address this limitation, we propose a novel approach that leverages existing pre-trained models. Our method first colors the sketches using a well-performing pre-trained condition-based image generation model, and then applies style transfer to achieve the desired artistic effect. Experimental results demonstrate that our method effectively generates stylized sketch images with high quality, without the overhead of additional training.

Yonsei University (KR)
Yonsei University
Openalex Percentile: Top 79%
Aesthetic Perception and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.