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Jung-Jae Yu

dblp:17/9717 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

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 · 1 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author

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.

Artificial intelligence
1 paper
Generative modeling · 100%
Computer graphics and multimedia
2 papers
Visual content generation and editing · 78% Virtual and augmented reality · 19% Geometric modeling and processing · 3%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
diffusion model
0.912025
Progressive Artwork Outpainting Via Latent Diffusion Models · ICCV 2025
Machine learning › Generative modeling › diffusion model
latent diffusion model
0.912025
Progressive Artwork Outpainting Via Latent Diffusion Models · ICCV 2025
Visual content generation and editing
image outpainting
0.912025
Progressive Artwork Outpainting Via Latent Diffusion Models · ICCV 2025
Virtual and augmented reality
augmented reality
0.112010
Camera motion tracking in a dynamic scene · ISMAR 2010
Virtual and augmented reality › tracking
camera tracking
0.112010
Camera motion tracking in a dynamic scene · ISMAR 2010
Geometric modeling and processing › 3d reconstruction
structure from motion
0.012010
Camera motion tracking in a dynamic scene · ISMAR 2010

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

latent diffusion · 1.7sfm · 0.1feature track reconstruction · 0.1auxiliary camera · 0.1
YearPublicationVenuePosition
2025 Progressive Artwork Outpainting Via Latent Diffusion Models
Dae-Young Song, Jung-Jae Yu, Donghyeon Cho
ICCV2
2010 Camera motion tracking in a dynamic scene
abstract
To insert a virtual object into a real image, the position of the object must appear seamlessly as the camera moves. This requires camera tracking with estimations of all internal and external parameters in each frame with an adequate degree of stability to ensure negligible visible drift between the real and virtual elements. In the post production of film, matchmoving software based on SfM is typically used in the camera tracking process. However, most of this type of software fails when attempting to track the camera in a dynamic scene in which a moving foreground object such as a real actor occupies a large part of the background. Therefore, this study proposes a camera tracking system that uses an auxiliary camera to estimate the motion of the main shooting camera and 3D position of background features in a dynamic scene. A novel reconstruction and connection method was developed for feature tracks that are occluded by a foreground object. Experimentation with a 2K sequence demonstrated the feasibility of the proposed method.
Jung-Jae Yu, Jae-Hean Kim
ISMAR1