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Gwangjin Ju

dblp:297/4107 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0003-1006-8351ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visual content generation and editing · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visual content generation and editing › image generation › stylized image generation
caricature generation
0.512021
StyleCariGAN: caricature generation via StyleGAN feature map modulation · ACM Trans. Graph. 2021

Methods — techniques the papers use, named apart from their topics

layer mixing · 0.5StyleGAN · 0.5
YearPublicationVenuePosition
2022 Self-Supervised Dehazing Network Using Physical Priors
Gwangjin Ju, Yeongcheol Choi, Jee Hyun Paik, Gyeongha Hwang, Seungyong Lee 0001
ACCV (3)1
2021 StyleCariGAN: caricature generation via StyleGAN feature map modulation
abstract
We present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed StyleCariGAN , automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type. The key component of our method is shape exaggeration blocks that are used for modulating coarse layer feature maps of StyleGAN to produce desirable caricature shape exaggerations. We first build a layer-mixed StyleGAN for photo-to-caricature style conversion by swapping fine layers of the StyleGAN for photos to the corresponding layers of the StyleGAN trained to generate caricatures. Given an input photo, the layer-mixed model produces detailed color stylization for a caricature but without shape exaggerations. We then append shape exaggeration blocks to the coarse layers of the layer-mixed model and train the blocks to create shape exaggerations while preserving the characteristic appearances of the input. Experimental results show that our StyleCariGAN generates realistic and detailed caricatures compared to the current state-of-the-art methods. We demonstrate StyleCariGAN also supports other StyleGAN-based image manipulations, such as facial expression control.
Wonjong Jang, Gwangjin Ju, Yucheol Jung, Jiaolong Yang, Xin Tong 0001, Seungyong Lee 0001
ACM Trans. Graph.2