Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tian-Yi Wang
dblp:171/6799
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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 |
Rendering · 67% Visual content generation and editing · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering
appearance modeling |
0.2 | 1 | 2015 | Magic decorator: automatic material suggestion for indoor digital scenes · ACM Trans. Graph. 2015 |
Rendering
material appearance |
0.2 | 1 | 2015 | Magic decorator: automatic material suggestion for indoor digital scenes · ACM Trans. Graph. 2015 |
Visual content generation and editing › scene synthesis
material assignment |
0.2 | 1 | 2015 | Magic decorator: automatic material suggestion for indoor digital scenes · ACM Trans. Graph. 2015 |
Methods — techniques the papers use, named apart from their topics
rule learning · 0.2combinatorial optimization · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Magic decorator: automatic material suggestion for indoor digital scenesabstractAssigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator , a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules , which describe typical material patterns for a small group of objects or parts, and global aesthetic rules , which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists. Kun Xu 0003, Yizhou Yu, Tian-Yi Wang, Shi-Min Hu 0001 |
ACM Trans. Graph. | 4 |