Demo Data from SIGIR 2026 / reviewers in the wild / expert
Dawon Lee
dblp:292/5552
· DBLP profile ↗
6ranked-venue papers
3as first author
6since 2021 · last 2026
0000-0002-7079-3769ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 5 · 2 first-author · 5 since 2021Graphics, 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
5 papers |
Multimedia analysis and retrieval · 40% Multimedia systems and quality of experience · 31% Visual content generation and editing · 16% | |
| Human-computer interaction and pervasive computing
2 papers |
Interaction techniques and input · 42% Usability and user experience research · 42% Collaborative and social computing · 16% |
Topics — the 10 heaviest of 13, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia systems and quality of experience › multimedia presentation
video playback |
1.3 | 2 | 2026 | ComVi: Context-Aware Optimized Comment Display in Video Playback · CHI 2026 Generating Highlight Videos of a User-Specified Length using Most Replayed Data · CHI 2025 |
Multimedia analysis and retrieval › audio-visual learning
audio-visual correspondence |
1.0 | 1 | 2026 | ComVi: Context-Aware Optimized Comment Display in Video Playback · CHI 2026 |
Multimedia analysis and retrieval › video summarization
highlight video generation |
0.9 | 1 | 2025 | Generating Highlight Videos of a User-Specified Length using Most Replayed Data · CHI 2025 |
Multimedia analysis and retrieval
video summarization |
0.9 | 1 | 2025 | Generating Highlight Videos of a User-Specified Length using Most Replayed Data · CHI 2025 |
Usability and user experience research
human performance modeling |
0.8 | 1 | 2024 | User Performance in Consecutive Temporal Pointing: An Exploratory Study · CHI 2024 |
Interaction techniques and input › target selection
pointing |
0.8 | 1 | 2024 | User Performance in Consecutive Temporal Pointing: An Exploratory Study · CHI 2024 |
Visual content generation and editing
video editing |
0.6 | 1 | 2022 | PopStage: The Generation of Stage Cross-Editing Video Based on Spatio-Temporal Matching · ACM Trans. Graph. 2022 |
Visual content generation and editing
3d content creation |
0.5 | 1 | 2021 | Virtual Camera Layout Generation using a Reference Video · CHI 2021 |
Virtual and augmented reality › immersive interaction › 3d interaction
virtual camera control |
0.5 | 1 | 2021 | Virtual Camera Layout Generation using a Reference Video · CHI 2021 |
Collaborative and social computing › social media
online video platforms |
0.3 | 1 | 2026 | ComVi: Context-Aware Optimized Comment Display in Video Playback · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
optimization · 2.0user study · 1.3most replayed data · 0.9chunking algorithm · 0.9spatio-temporal matching · 0.6dynamic programming · 0.6reference video analysis · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | ComVi: Context-Aware Optimized Comment Display in Video PlaybackabstractOn general video-sharing platforms like YouTube, comments are displayed independently of video playback. As viewers often read comments while watching a video, they may encounter ones referring to moments unrelated to the current scene, which can reveal spoilers and disrupt immersion. To address this problem, we present ComVi, a novel system that displays comments at contextually relevant moments, enabling viewers to see time-synchronized comments and video content together. We first map all comments to relevant video timestamps by computing audio-visual correlation, then construct the comment sequence through an optimization that considers temporal relevance, popularity (number of likes), and display duration for comfortable reading. In a user study, ComVi provided a significantly more engaging experience than conventional video interfaces (i.e., YouTube and Danmaku), with 71.9% of participants selecting ComVi as their most preferred interface. Minsun Kim, Dawon Lee, Jun-yong Noh |
CHI | 2 |
| 2025 | Generating Highlight Videos of a User-Specified Length using Most Replayed Data
Minsun Kim, Dawon Lee, Jun-yong Noh |
CHI | 2 |
| 2025 | OptiSub: Optimizing Video Subtitle Presentation for Varied Display and Font Sizes via Speech Pause-Driven Chunking
Dawon Lee, Jun-yong Noh |
CHI | 1 |
| 2024 | User Performance in Consecutive Temporal Pointing: An Exploratory StudyabstractA significant amount of research has recently been conducted on user performance in so-called temporal pointing tasks, in which a user is required to perform a button input at the timing required by the system. Consecutive temporal pointing (CTP), in which two consecutive button inputs must be performed while satisfying temporal constraints, is common in modern interactions, yet little is understood about user performance on the task. Through a user study involving 100 participants, we broadly explore user performance in a variety of CTP scenarios. The key finding is that CTP is a unique task that cannot be considered as two ordinary temporal pointing processes. Significant effects of button input method, motor limitations, and different hand use were also observed. Dawon Lee, Sunjun Kim, Jun-yong Noh, Byungjoo Lee |
CHI | 1 |
| 2022 | PopStage: The Generation of Stage Cross-Editing Video Based on Spatio-Temporal MatchingabstractStageMix is a mixed video that is created by concatenating the segments from various performance videos of an identical song in a visually smooth manner by matching the main subject's silhouette presented in the frame. We introduce PopStage , which allows users to generate a StageMix automatically. PopStage is designed based on the StageMix Editing Guideline that we established by interviewing creators as well as observing their workflows. PopStage consists of two main steps: finding an editing path and generating a transition effect at a transition point. Using a reward function that favors visual connection and the optimality of transition timing across the videos, we obtain the optimal path that maximizes the sum of rewards through dynamic programming. Given the optimal path, PopStage then aligns the silhouettes of the main subject from the transitioning video pair to enhance the visual connection at the transition point. The virtual camera view is next optimized to remove the black areas that are often created due to the transformation needed for silhouette alignment, while reducing pixel loss. In this process, we enforce the view to be the maximum size while maintaining the temporal continuity across the frames. Experimental results show that PopStage can generate a StageMix of a similar quality to those produced by professional creators in a highly reduced production time. Dawon Lee, Jung Eun Yoo, Kyungmin Cho, Bumki Kim, Gyeonghun Im, Jun-yong Noh |
ACM Trans. Graph. | 1 |
| 2021 | Virtual Camera Layout Generation using a Reference VideoabstractWe propose a method that generates a virtual camera layout of a 3D animation scene by following the cinematic intention of a reference video. From a reference video, cinematic features such as the start frame, end frame, framing, camera movement, and the visual features of the subjects are extracted automatically. The extracted information is used to generate the virtual camera layout, which resembles the camera layout of the reference video. Our method handles stylized as well as human characters with body proportions different from those of humans. We demonstrate the effectiveness of our approach with various reference videos and 3D animation scenes. The user evaluation results show that the generated layouts are comparable to layouts created by the artist, allowing us to assert that our method can provide effective assistance to both novice and professional users when positioning a virtual camera. Jung Eun Yoo, Kwanggyoon Seo, Sanghun Park, Jaedong Kim, Dawon Lee, Jun-yong Noh |
CHI | 5 |