Demo Data from SIGIR 2026 / reviewers in the wild / expert
Manwen Li
dblp:326/3607
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
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.
| Computer graphics and multimedia
1 paper |
Computer animation and physical simulation · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer animation and physical simulation
cloth simulation |
0.6 | 1 | 2022 | Estimation of yarn-level simulation models for production fabrics · ACM Trans. Graph. 2022 |
Computer animation and physical simulation › physically-based modeling
material modeling |
0.6 | 1 | 2022 | Estimation of yarn-level simulation models for production fabrics · ACM Trans. Graph. 2022 |
Computer animation and physical simulation › cloth simulation
yarn-level cloth |
0.6 | 1 | 2022 | Estimation of yarn-level simulation models for production fabrics · ACM Trans. Graph. 2022 |
Methods — techniques the papers use, named apart from their topics
swatch-level approximation · 0.6physical testing · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Estimation of yarn-level simulation models for production fabricsabstractThis paper introduces a methodology for inverse-modeling of yarn-level mechanics of cloth, based on the mechanical response of fabrics in the real world. We compiled a database from physical tests of several different knitted fabrics used in the textile industry. These data span different types of complex knit patterns, yarn compositions, and fabric finishes, and the results demonstrate diverse physical properties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation. To do so, we introduce an efficient pipeline for converting between fabric-level data and yarn-level simulation, including a novel swatch-level approximation for speeding up computation, and some small-but-necessary extensions to yarn-level models used in computer graphics. The dataset used for this paper can be found at http://mslab.es/projects/YarnLevelFabrics. Georg Sperl, Rosa María Sánchez-Banderas, Manwen Li, Christopher Wojtan, Miguel A. Otaduy |
ACM Trans. Graph. | 3 |