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Manwen Li

dblp:326/3607 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer animation and physical simulation
cloth simulation
0.612022
Estimation of yarn-level simulation models for production fabrics · ACM Trans. Graph. 2022
Computer animation and physical simulation › physically-based modeling
material modeling
0.612022
Estimation of yarn-level simulation models for production fabrics · ACM Trans. Graph. 2022
Computer animation and physical simulation › cloth simulation
yarn-level cloth
0.612022
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
YearPublicationVenuePosition
2022 Estimation of yarn-level simulation models for production fabrics
abstract
This 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