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Tian-Yi Wang

dblp:171/6799 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Rendering
appearance modeling
0.212015
Magic decorator: automatic material suggestion for indoor digital scenes · ACM Trans. Graph. 2015
Rendering
material appearance
0.212015
Magic decorator: automatic material suggestion for indoor digital scenes · ACM Trans. Graph. 2015
Visual content generation and editing › scene synthesis
material assignment
0.212015
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
YearPublicationVenuePosition
2015 Magic decorator: automatic material suggestion for indoor digital scenes
abstract
Assigning 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