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Minseong Kwon

dblp:402/0259 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer animation and physical simulation › motion synthesis
motion interpolation
0.912025
TSDF-Based Efficient Motion-Compensated Temporal Interpolation for 3D Dynamic Sequences · AAAI 2025
Image and video processing › video frame interpolation
temporal interpolation
0.912025
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
YearPublicationVenuePosition
2025 TSDF-Based Efficient Motion-Compensated Temporal Interpolation for 3D Dynamic Sequences
abstract
This 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
AAAI2