Demo Data from EDBT 2026 / reviewers in the wild / expert
Tan-Hiep To
dblp:371/6467
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0008-1264-1354ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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 |
Visual content generation and editing · 50% Virtual and augmented reality · 25% Geometric modeling and processing · 25% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › avatar
avatar animation |
0.9 | 1 | 2025 | Streamlining Virtual KOL Generation Through Modular Generative AI Architecture · ACM Multimedia 2025 |
Geometric modeling and processing › shape modeling › garment modeling
garment generation |
0.9 | 1 | 2025 | Streamlining Virtual KOL Generation Through Modular Generative AI Architecture · ACM Multimedia 2025 |
Visual content generation and editing › style transfer
makeup transfer |
0.9 | 1 | 2025 | Streamlining Virtual KOL Generation Through Modular Generative AI Architecture · ACM Multimedia 2025 |
Methods — techniques the papers use, named apart from their topics
modular service architecture · 0.9generative AI · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Streamlining Virtual KOL Generation Through Modular Generative AI ArchitectureabstractKey Opinion Leaders (KOLs) play a crucial role in modern marketing by shaping consumer perceptions and enhancing brand credibility. However, collaborating with human KOLs often involves high costs and logistical challenges. To address this, we present GenKOL, an interactive system that empowers marketing professionals to efficiently generate high-quality virtual KOL images using generative AI. GenKOL enables users to dynamically compose promotional visuals through an intuitive interface that integrates multiple AI capabilities, including garment generation, makeup transfer, background synthesis, and hair editing. These capabilities are implemented as modular, interchangeable services that can be deployed flexibly on local machines or in the cloud. This modular architecture ensures adaptability across diverse use cases and computational environments. Our system can significantly streamline the production of branded content, lowering costs and accelerating marketing workflows through scalable virtual KOL creation. Video demo is available at https://youtu.be/uXpXmEbjg3M. Tan-Hiep To, Duy-Khang Nguyen, Minh-Triet Tran, Trung-Nghia Le |
ACM Multimedia | 1 |