EDBT 2026 Demo / reviewers in the wild / expert
Sherry Qiu
dblp:299/1252
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, 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.
| Human-computer interaction and pervasive computing
1 paper |
Interaction techniques and input · 100% | |
| Computer graphics and multimedia
1 paper |
Rendering · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering
non-photorealistic rendering |
0.5 | 1 | 2021 | Tracing versus freehand for evaluating computer-generated drawings · ACM Trans. Graph. 2021 |
Interaction techniques and input
freehand drawing |
0.5 | 1 | 2021 | Tracing versus freehand for evaluating computer-generated drawings · ACM Trans. Graph. 2021 |
Interaction techniques and input
pen input |
0.5 | 1 | 2021 | Tracing versus freehand for evaluating computer-generated drawings · ACM Trans. Graph. 2021 |
Methods — techniques the papers use, named apart from their topics
stroke feature analysis · 1.0spatiotemporal analysis · 0.5spatio-temporal analysis · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Tracing versus freehand for evaluating computer-generated drawingsabstractNon-photorealistic rendering (NPR) and image processing algorithms are widely assumed as a proxy for drawing. However, this assumption is not well assessed due to the difficulty in collecting and registering freehand drawings. Alternatively, tracings are easier to collect and register, but there is no quantitative evaluation of tracing as a proxy for freehand drawing. In this paper, we compare tracing, freehand drawing, and computer-generated drawing approximation (CGDA) to understand their similarities and differences. We collected a dataset of 1,498 tracings and freehand drawings by 110 participants for 100 image prompts. Our drawings are registered to the prompts and include vector-based timestamped strokes collected via stylus input. Comparing tracing and freehand drawing, we found a high degree of similarity in stroke placement and types of strokes used over time. We show that tracing can serve as a viable proxy for freehand drawing because of similar correlations between spatio-temporal stroke features and labeled stroke types. Comparing hand-drawn content and current CGDA output, we found that 60% of drawn pixels corresponded to computer-generated pixels on average. The overlap tended to be commonly drawn content, but people's artistic choices and temporal tendencies remained largely uncaptured. We present an initial analysis to inform new CGDA algorithms and drawing applications, and provide the dataset for use by the community. Zeyu Wang 0003, Sherry Qiu, Nicole Feng, Holly E. Rushmeier, Leonard McMillan, Julie Dorsey |
ACM Trans. Graph. | 2 |