Demo Data from EDBT 2026 / reviewers in the wild / expert
Seunguk Do
dblp:405/7642
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
3d reconstruction |
0.9 | 1 | 2025 | ShowMak3r: Compositional TV Show Reconstruction · CVPR 2025 |
Computer vision › 3D vision
3d scene understanding |
0.9 | 1 | 2025 | ShowMak3r: Compositional TV Show Reconstruction · CVPR 2025 |
Computer vision › 3D vision › 3d human reconstruction
human performance reconstruction |
0.9 | 1 | 2025 | ShowMak3r: Compositional TV Show Reconstruction · CVPR 2025 |
Visual content generation and editing
video editing |
0.3 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | ShowMak3r: Compositional TV Show ReconstructionabstractReconstructing 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 |
CVPR | 2 |