EDBT 2026 Demo / reviewers in the wild / expert
Yuencheng Lee
dblp:19/4701
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1995
—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 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author
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 |
Geometric modeling and processing · 67% Computer animation and physical simulation · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing
3d face modeling |
0.0 | 1 | 1995 | Realistic modeling for facial animation · SIGGRAPH 1995 |
Computer animation and physical simulation
facial animation |
0.0 | 1 | 1995 | Realistic modeling for facial animation · SIGGRAPH 1995 |
Geometric modeling and processing › point cloud processing
range image processing |
0.0 | 1 | 1995 | Realistic modeling for facial animation · SIGGRAPH 1995 |
Methods — techniques the papers use, named apart from their topics
muscle simulation · 0.0laser-scanned range data fitting · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Realistic modeling for facial animationabstractA major unsolved problem in computer graphics is the construction and animation of realistic human facial models. Traditionally, facial models have been built painstakingly by manual digitization and animated by ad hoc parametrically controlled facial mesh deformations or kinematic approximation of muscle actions. Fortunately, animators are now able to digitize facial geometries through the use of scanning range sensors and animate them through the dynamic simulation of facial tissues and muscles. However, these techniques require considerableuser input to construct facial models of individuals suitable for animation. In this paper, we present a methodology for automating this challenging task. Starting with a structured facial mesh, we develop algorithms that automatically construct functional models of the heads of human subjects from laser-scanned range and reflectance data. These algorithms automatically insert contractile muscles at anatomically correct positions within a dynamic skin model and root them in an estimated skull structure with a hinged jaw. They also synthesize functional eyes, eyelids, teeth, and a neck and fit them to the final model. The constructed face may be animated via muscle actuations. In this way, we create the most authentic and functional facial models of individuals available to date and demonstrate their use in facial animation. Yuencheng Lee, Demetri Terzopoulos, Keith Waters |
SIGGRAPH | 1 |