TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Danner, Michael
A1  - Brake, Elena
A1  - Kosel, Gabriela
A1  - Kyosev, Yordan
A1  - Rose, Katerina
A1  - Rätsch, Matthias
A1  - Cebulla, Holger
T1  - AI-assisted pattern generator for garment design
JF  - Communications in development and assembling of textile products
N2  - This paper introduces an AI-assisted pattern generator, aimed to simplify garment design by flattening the pattern creation in an automated process from 3D scans for users without knowledge of conventional pattern construction. This garment tool plug-in computerizes the development of scanned persons into 3D shell surface meshes, which are automatically unwrapped into 2D patterns, streamlining the traditionally complex aspects of garment design for novices. The process uses advanced AI algorithms to facilitate the conversion of 3D scans into usable patterns. Machine learning adapts to different garment styles (close-fitting, regular fit and loose-fitting), ensuring a broad applicability, while customization options allow a precise adaption to individual body measurements. This AI-assisted tool enables a wider audience to generate customized garment creation.
KW  - ai-assisted pattern construction
KW  - pattern generation
KW  - CAD flattening
KW  - machine learning
Y1  - 2024
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-54553
SN  - 2701-939X
SS  - 2701-939X
U6  - https://doi.org/10.25367/cdatp.2024.5.p195-206
DO  - https://doi.org/10.25367/cdatp.2024.5.p195-206
VL  - 5
IS  - 2
SP  - 195
EP  - 206
S1  - 12
PB  - Technische Universität Chemnitz
CY  - Dresden
ER  -