Demo Data from VLDB 2026 / reviewers in the wild / expert
Seung Won Lee 0004
dblp:87/9241-4
· DBLP profile ↗
3ranked-venue papers
2as first author
3since 2021 · last 2025
0000-0001-6435-0833ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 2 since 2021Databases, data management, data science and information retrieval · 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.
| Human-computer interaction and pervasive computing
2 papers |
Human-AI interaction · 38% User interface design and tools · 33% Design research and methods · 29% | |
| Computer graphics and multimedia
1 paper |
Geometric modeling and processing · 100% |
Topics — the 3 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
User interface design and tools › authoring tools
3d design tools |
0.9 | 1 | 2025 | GenPara: Enhancing the 3D Design Editing Process by Inferring Users' Regions of Interest with Text-Conditional Shape Parameters · CHI 2025 |
Design research and methods › design space
design space exploration |
0.8 | 1 | 2024 | The Impact of Sketch-guided vs. Prompt-guided 3D Generative AIs on the Design Exploration Process · CHI 2024 |
Human-AI interaction › generative AI
generative AI in design |
0.8 | 1 | 2024 | The Impact of Sketch-guided vs. Prompt-guided 3D Generative AIs on the Design Exploration Process · CHI 2024 |
Methods — techniques the papers use, named apart from their topics
large language model · 1.7bayesian inference · 1.7workflow analysis · 0.8linkography · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | GenPara: Enhancing the 3D Design Editing Process by Inferring Users' Regions of Interest with Text-Conditional Shape ParametersabstractIn 3D design, specifying design objectives and visualizing complex shapes through text alone proves to be a significant challenge. Although advancements in 3D GenAI have significantly enhanced part assembly and the creation of high-quality 3D designs, many systems still to dynamically generate and edit design elements based on the shape parameters. To bridge this gap, we propose GenPara, an interactive 3D design editing system that leverages text-conditional shape parameters of part-aware 3D designs and visualizes design space within the Exploration Map and Design Versioning Tree. Additionally, among the various shape parameters generated by LLM, the system extracts and provides design outcomes within the user's regions of interest based on Bayesian inference. A user study N = 16 revealed that \textit{GenPara} enhanced the comprehension and management of designers with text-conditional shape parameters, streamlining design exploration and concretization. This improvement boosted efficiency and creativity of the 3D design process. Jiin Choi 0001, Seung Won Lee 0004, Kyung Hoon Hyun |
CHI | 2 |
| 2024 | The Impact of Sketch-guided vs. Prompt-guided 3D Generative AIs on the Design Exploration ProcessabstractVarious modalities have emerged in the field of 3D generative AI (GenAI) to enhance design outcomes. While some designers find inspiration in prompts to guide their design options, others prefer sketching to embody creative visions. Nonetheless, the impact of the different modalities of 3D GenAI on the design process remains largely unexplored. This study examines the utilization of prompt- and sketch-guided modalities within the design process by conducting linkography and workflow analyses with 12 designers. The results revealed that prompts played a pivotal role in stimulating initial ideation, whereas sketches played a crucial role in embodying design ideas. This investigation highlights the distinct contributions of these modalities at different phases of the design process, suggesting the potential for a more refined and synergistic collaboration between humans and AI. By elucidating the diverse functions of sketches and prompts, we propose prospective directions for the UX framework of the 3D GenAI. Seung Won Lee 0004, Tae Hee Jo, Semin Jin, Jiin Choi 0001, Kyungwon Yun, Sergio Bromberg, Seonghoon Ban, Kyung Hoon Hyun |
CHI | 1 |
| 2023 | BIGaze: An eye-gaze action-guided Bayesian information gain framework for information exploration
Seung Won Lee 0004, Hwan Kim, Taeha Yi, Kyung Hoon Hyun |
Adv. Eng. Informatics | 1 |