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Hojoon Jung

dblp:424/5145 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0003-2843-9138ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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
Rendering · 87% Geometric modeling and processing · 13%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d scene reconstruction
1.012026
Through the Water: Refractive Gaussian Splatting for Water Surface Scenes · AAAI 2026
Computer vision › 3D vision › 3d reconstruction › surface reconstruction
water surface reconstruction
1.012026
Through the Water: Refractive Gaussian Splatting for Water Surface Scenes · AAAI 2026
Rendering
gaussian splatting
1.012026
Through the Water: Refractive Gaussian Splatting for Water Surface Scenes · AAAI 2026
Rendering
neural rendering
1.012026
Through the Water: Refractive Gaussian Splatting for Water Surface Scenes · AAAI 2026

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

soft mask · 2.0dual gaussian primitives · 2.02d gaussian ray tracing · 2.0
YearPublicationVenuePosition
2026 Through the Water: Refractive Gaussian Splatting for Water Surface Scenes
abstract
Scenes with water surfaces present a significant challenge for Gaussian Splatting due to the simultaneous presence of refraction and reflection, as well as the difficulty of accurately estimating the geometry of transparent water surfaces. To address this, we propose a novel framework for reconstructing scenes involving both reflection and refraction caused by water surfaces. The water surface is modeled as a trainable plane, and 2D Gaussian ray tracing is applied to account for refraction through the water. We extend 2D Gaussian Splatting by introducing a soft mask parameter and a dual set of Gaussian primitives, which handle both reflected and refracted effects. Our method achieves state-of-the-art performance on newly constructed water surface datasets, including both synthetic and real scenes, and significantly outperforms prior approaches in water-interacting regions. Furthermore, we demonstrate the editability of our model by manipulating the index of refraction to suppress or modify refractive effects, enabling scene transformations into different liquids.
Yeonghun Yoon, Hojoon Jung, Jaeyoon Lee, Taegwan Kim, Gyu-Hyun Kim
AAAI2
2026 3D-aware virtual try-on using only 2D inputs
Jaeyoon Lee, Hojoon Jung, Jongwon Choi 0002
Comput. Vis. Image Underst.2