Demo Data from VLDB 2026 / reviewers in the wild / expert
Minseong Kwon
dblp:402/0259
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved
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.
| Computer graphics and multimedia
1 paper |
Computer animation and physical simulation · 33% Image and video processing · 33% Geometric modeling and processing · 33% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer animation and physical simulation › motion synthesis
motion interpolation |
0.9 | 1 | 2025 | TSDF-Based Efficient Motion-Compensated Temporal Interpolation for 3D Dynamic Sequences · AAAI 2025 |
Image and video processing › video frame interpolation
temporal interpolation |
0.9 | 1 | 2025 | TSDF-Based Efficient Motion-Compensated Temporal Interpolation for 3D Dynamic Sequences · AAAI 2025 |
Methods — techniques the papers use, named apart from their topics
truncated signed distance function · 0.9motion compensation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | TSDF-Based Efficient Motion-Compensated Temporal Interpolation for 3D Dynamic SequencesabstractThis paper introduces a method for efficiently interpolating 3D dynamic sequences using truncated signed distance function (TSDF) volumes. The method calculates bi-directional motions between TSDF volumes of two frames and refines them to reconstruct intermediate frames. Unlike point cloud-based methods, which can suffer from varying and irregular point densities, the uniform and dense grid structure of TSDF offers a consistent framework for estimating the true motion of objects within a scene. In our experiments, the TSDF-based method offers more precise and reliable smooth motion prediction compared to the often error-prone surface depiction in point clouds. Experimental results demonstrate improved accuracy and reduced computational complexity, making it suitable for real-time applications. Soowoong Kim, Minseong Kwon, Gun Bang, Seungjoon Yang |
AAAI | 2 |