Textile-GAN: Texture synthesis based on aesthetic evaluation with generative adversarial networks
Fumika Taniguchi, Kensuke Tobitani, Noriko Nagata

Nagata Laboratory, Department of Human System Interaction, Graduate School of Science and Technology, Kwansei Gakuin University (KGU), JapanIntroduction

 Background---The promotion of design that respects
each person’s individualityReduction of human and resource costs
Increased individual and social well-being Mixable image features by Style

Estimated to be highly expressive in the conditioning of pattern images

Quantification of visual impressions (aesthetics)1

“Style Mixing”

 Ex.) Human faces (age, gender,with or without glasses, hair color)

In the field of product design, it is important to understanduser needs and reflect them in products
- The expressive power of Style in StyleGAN[1] Impression Estimation Model for Clothing Patterns
Using Neural Style Features（Sunda et al., 2020）

With glasses, middle-aged, maleTexture generation technologyNo technology has been established to generate textures forgarments with the desired impression.
Focus on GANs (generative adversarial networks)

GlassesGender Purpose-Proposed Method？

Generated
image

Applies toour model

Textile-GAN: a method for generating textures (patterns)
with the desired aesthetic texture？
？

AgeConditioning impression of generated images
Hair color

Style-Mixing example(3) Texture synthesis based on affective evaluation

 Conditioning of generated images-Evaluation words: “Regular,” “Beautiful”

 “Not very regular” ~ “Very regular”“Not very beautiful” ~ “Very beautiful”
7-point Likert scale [score: -3 ~ +3] in (1) experiment
Conditioning impression of generated images(1) Quantification of Visual Impressions (aesthetics)

 Impression evaluation experiment using crowdsourcing-Participants: 1,188-Evaluation words: 28 impression words-Stimuli: 4,036 pattern images

Procedure: 7-point Likert scale

Plot on 2 axes
“Regular,” “Beautiful”

 Factor analysis-

6 factors were extracted

Conditions the impressionof this image

1. Geometrical・・・・

2. Stylish

Conditions the impressionof this image

Regular

Regular

Factoranalysis 3. Fluffy
4. Pop(1) Quantification ofVisual Impressions

5. Old-fashionedNot
Beautiful

6. Complexity

Beautiful

Not
BeautifulBeautiful(2) Construction of Texture Generation Models
 Construction of texture generation models using StyleGAN-

Version of StyleGAN：StyleGAN2-ADA
 GAN using style transfer technology (Style) High-resolution images can be generated-StyleQuantification of visual impressions
using construction models
Impression conditioning of generated images by Style Future study

impression values
representing the degree of conformity
to each impression word in (1) experiment-

StyleGAN2-ADA

Conclusion

NotRegular Propose a method for generating textures (patterns)
with the desired aesthetic texture MethodsInput images : 4,036 pattern images

NotRegular・・・

Impression conditioning of generated images by Style is inferred to be possible

Evaluate and improve the generated images’ quality andcondition them

Repeatability of the generated pattern image
Analysis of whether the evaluation terms
are correctly reflected