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Computer Graphics Forum
Article . 2024 . Peer-reviewed
License: CC BY
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Article . 2024
License: CC BY
DBLP
Article
Data sources: DBLP
DBLP
Article
Data sources: DBLP
Computer Graphics Forum
Article . 2024 . Peer-reviewed
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Computational Smocking through Fabric‐Thread Interaction

Authors: Ningfeng Zhou; Jing Ren 0004; Olga Sorkine-Hornung;

Computational Smocking through Fabric‐Thread Interaction

Abstract

AbstractWe formalize Italian smocking, an intricate embroidery technique that gathers flat fabric into pleats along meandering lines of stitches, resulting in pleats that fold and gather where the stitching veers. In contrast to English smocking, characterized by colorful stitches decorating uniformly shaped pleats, and Canadian smocking, which uses localized knots to form voluminous pleats, Italian smocking permits the fabric to move freely along the stitched threads following curved paths, resulting in complex and unpredictable pleats with highly diverse, irregular structures, achieved simply by pulling on the threads. We introduce a novel method for digital previewing of Italian smocking results, given the thread stitching path as input. Our method uses a coarse‐grained mass‐spring system to simulate the interaction between the threads and the fabric. This configuration guides the fine‐level fabric deformation through an adaptation of the state‐of‐the‐art simulator, C‐IPC [LKJ21]. Our method models the general problem of fabric‐thread interaction and can be readily adapted to preview Canadian smocking as well. We compare our results to baseline approaches and physical fabrications to demonstrate the accuracy of our method.

Countries
Switzerland, Switzerland
Related Organizations
Keywords

Computer Science - Graphics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
hybrid
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