Building an Intelligent Garment Shopping Platform for Personalized Cooperative Design By Xianyi Zeng, Pascal Bruniaux and Xiao Chen ENSAIT Textile Institute, Roubaix, France
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Building an intelligent garment e-shop
Current garment e-shopping (>40% of benefits in Europe): 1) E-catalogs based on classification 2) Visualization: photos (mostly) and 3D virtual objects 3) Virtual perception: visual effects only, static fitting
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Building an intelligent garment e-shop
E-shopping in the future: - More complete virtual environment => 1) Personalized fitting 2) Virtual perception: visual effects (static and dynamic) + fabric hand + comfort + controlled virtual ambiance 3) Virtual sales advisor => professional product/consumer knowledge + Searching engine: personalized product recommender system
- A cooperative garment design platform => 1) Interactions between consumer, designer and material developer 2) Searching engine: from consumer’s needs to new technical parameters (styles, patterns, fabrics, colors, textures, …): personalized design recommender system
3
Personalized cooperative design Extension to a new production model (small series, fast fashion) Task allocation, scheduling
Transporter 1
LCA
Mini factory 1
LOGISTIC PLATEFORM
Stock 1
Body shape classification
International supply chain
production orders
Technical Constraints cost
CO-DESIGN PLATEFORM
Ease allowance comfort Real & virtual shop
Recommendatio n
Product tracking
Perception& emotion modeling Virtual fitting process 4
Personalized cooperative design Personalized virtual fitting process 1
4
2
3
5
7
6
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Co-design : new concept for garment design and production, man/garment interface
Garment CAD software: inputs => fabric mechanical and optical properties garment patterns outputs => static and dynamic virtual garments 5
Personalized cooperative design Personalized virtual fitting process Photo
Modaris
Optitex
Clo3D
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Personalized cooperative design Classification of parametric 3D body shapes Lost of information with classical classification procedures
Comparison between fuzzy pattern (blue line) and classical pattern (red line) 10
Personalized cooperative design Co-design: control of perception by adjusting technical parameters
Inputs:
Outputs:
Stretch-weft Stretch-warp Shear Bending-weft Bending-warp Buckling ratio Bulking-stiffness
Fiber perception: naturel or synthetic
Ʃ
Process perception: weaving or knitting Flexible - Rigid Rough – Smooth Soft (surface) - Hard Draping perception Thick - Thin
Basic mass
Heavy - Light
Thickness
Brillant - Matt Hot color – Cold color
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Personalized cooperative design Co-design: control of perception by adjusting technical parameters Initial prototype
Bending 10=>30
Shearing 5=>30
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Personalized cooperative design Evaluating fabric hand from virtual prototypes Modelling visual/tactile interactions - Interpret tactile information from virtual prototype - Adjust model parameters according to the desired tactile property in order to design the most appropriate material
Upper part Pliable
Very
Loose
Quite
Fuzzy
Fairly
Thin
Quite
Stretchy
Fairly
Lower part Tactile information
Pliable
Fairly
Tight
Fairly
Slippery
Very
Thin
Quite
Draped
Quite 13
Personalized cooperative design Recommender System: evaluating a new style relative to a specific body shape and an expected fashion theme
Output of Model I Relevancy (BR, T)
Output of Model II Relevancy (BR_de, T)
whether a garment design is feasible for a specific body shape in terms of promotion of its relevancy to the fashion theme ? 14
Conclusion: Summary of our projects: - Consumer perception and emotion-oriented design - Integration of professional fashion knowledge - Integration of virtual reality and real sample’s data Consideration on fashion design of people with physical Limitations: - Identification of physical limitations from video – animation software - Characterization of consumer perception and emotion - Personalized virtual garment design process - Virtual fitting of consumers with integration of perceptions and emotions and physical limitations 17
