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Seunguk Do

dblp:405/7642 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 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.

Artificial intelligence
1 paper
3D vision · 100%
Computer graphics and multimedia
1 paper
Visual content generation and editing · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d reconstruction
0.912025
ShowMak3r: Compositional TV Show Reconstruction · CVPR 2025
Computer vision › 3D vision
3d scene understanding
0.912025
ShowMak3r: Compositional TV Show Reconstruction · CVPR 2025
Computer vision › 3D vision › 3d human reconstruction
human performance reconstruction
0.912025
ShowMak3r: Compositional TV Show Reconstruction · CVPR 2025
Visual content generation and editing
video editing
0.312025
ShowMak3r: Compositional TV Show Reconstruction · CVPR 2025

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

pose interpolation · 1.7neural radiance field · 1.7face fitting · 1.7depth estimation · 1.7
YearPublicationVenuePosition
2025 ShowMak3r: Compositional TV Show Reconstruction
abstract
Reconstructing dynamic radiance fields from video clips is challenging, especially when entertainment videos like TV shows are given. Many challenges make the reconstruction difficult due to (1) actors occluding with each other and having diverse facial expressions, (2) cluttered stages, and (3) small baseline views or sudden shot changes. To address these issues, we present ShowMak3r, a comprehensive reconstruction pipeline that allows the editing of scenes like how video clips are made in a production control room. In ShowMak3r, a 3DLocator module locates recovered actors on the stage using depth prior and estimates unseen human poses via interpolation. The proposed ShotMatcher module then tracks the actors under shot changes. Furthermore, ShowMak3r introduces a face-fitting network that dynamically recovers the actors’ expressions. Experiments on Sitcoms3D dataset show that our pipeline can reassemble TV show scenes with new cameras at different timestamps. We also demonstrate that ShowMak3r enables interesting applications such as synthetic shot-making, actor relocation, insertion, deletion, and pose manipulation. Project page : https://nstar1125.github.io/showmak3r
Seunguk Do, Jaesik Park
CVPR2