Crowd Sourcing Clothes Design Directed byAdversarial Neural Networks
Natsumi KatoHiroyuki Osone∗

Daitetsu SatoNaoya Muramatsu

Yoichi Ochiai
University of Tsukuba
Pixie Dust Technologies, Inc.

Abstract
Deep neural networks (DNNs) applications are now increasingly pervasive andpowerful. However, fashion designers are lagging behind in leveraging this increasingly common technology. DNNs are not yet a standard part of fashion design
practice, in either clothes patterns or prototyping tools. In this paper, we present
DeepWear, a method using deep convolutional generative adversarial networks(DCGANs) for clothes design. The DNNs learn the feature of specific brand clothes
and generate images, then patterns instructed from the images are made, and anauthor creates clothes based on that. We evaluated this system by evaluating thecredibility of the actual sold clothes on market with our clothes. As the result, wefound it is possible to make clothes look like actual products from the generatedimages. Our findings have implications for collaborative design between machine
and human intelligence.

Conventional Method
Input

DCGANsGenerator

Design Sketch

Patterns Making

Clothes MakingDeConv

Fashion Show

Linear&Sigmoid

BatchNorm

DeConv&ReLU&BatchNorm&Reshape&ReLUz100524288

16*16*2048

32*32*1024

64*64*512

128*128*256

128*128*3

Discriminator

Conv

LeakyReLU

BatchNorm&Conv&LeakyReLU&Conv

BatchNorm&LeakyReLU&Linear

128*128*3

64*64*512

32*32*1024

16*16*2048

524288OutputFeedback

Clothes Making

Patterns Making

Generated Images

Our Contribution

Figure 1: Our system workflow.

1Introduction

Recent advances in computational fabrication have afforded the opportunity to use automated tools
and machines to support fashion design. However, finding inspiration is still a difficult task. In
this work, we present DeepWear, a practical system that uses DCGANs [1] to generate images fordesigners to make clothes by receiving instructions from those images. State-of-the-art learningtechniques such as DCGANs are first applied to the workflow of designing clothes. The system takes
specific brand [2] clothes images as the input, learns the feature of the input, generates images that∗Natsumi and Hiroyuki contributed equally to the work.

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

looks close to the clothes, then patterns are instructed from the images are made, and an author createsclothes based on that. In the evaluation, we conduct a content analysis of our system in practicality bycomparing the actual sold clothes with our clothes and other brand clothes by questioning which are
the specific brand clothes actual sold. The results show that our system is possible to make clothes
that look like actual products from the generated images. This paper shows the implications forcollaborative design between machine and human intelligence.

2Method

We collected clothes images of a specific brand announced between 2014 and 2017. We used web
scraping Python code to create the training dataset. As the result, over 1.1K images were collected.
Several steps were performed to be learned the feature of the images. We paint the background white
so that only people and clothes are cut out, and processed it into RGB image of 128 px x 128 px. We
use these images to generate new image by DCGANs. We followed implementation and training
procedure recent work by Radford et al. [1]. Training was done with a batch size of 7, using Adamwith hyperparameters (α = 0.0002, β1 = 0.5, β2 = 0.999,  = 10−8 ), and run on an NVIDIA Titan
X GPU for 1000 epochs. We stopped running around 43000 iteration (about 270 epoch), because theloss become quite small and generated images look good. At the 43000 iteration, Generator loss is
16.1374, discriminator loss is 1.39158. Generated images are shown in Fig. 2.
Seven partcipants(6 females and 1 male) aged between 21 and 23 partcipated. They are experienced
fashion design patterners and had clothing experience of 1 year and a half to 5 years (Patterners arepeople who draw patterns of clothes based on instructions from designers). We ask them to draw
patterns based on the generated images. These works were done under the presence of an author orby online calls. We set the time limit 70 minutes for all subjects. The pattern created for the first was
the image in Fig. 3 A, the second pattern was asked to draw the pattern based on the image selectedby each subject (shown in Fig 3 B). Based on the pattern that the patterner handwritten, using twokinds of black cloth, an author made three kinds of clothes. The working time was about 50 hours.

3Evaluation
Table 1: The result of the questionnaire that to distinguish the source brand clothes.
Ave
Other
DeepWear
Source brand80 (1)
121 (3)131 (2)78 (5)
87 (6)
120 (4)

97 (7)107 (8)115 (9)85
105(+23.5%)
122(+43.5%)

We conducted an experiment to evaluate whether the image generated from DCGANs can be instruction sources for creating a new clothes. The experiment is comparing and evaluating the quality of theactually sold clothes on market with our clothes and the other brand clothes. We prepared images ofthe source brand and output of our method and images of other brands2 . To the subjects, six clothesof the source brand were first exemplified. After that, we asked clothing images one by one in random
order and evaluated whether or not the displayed image (shown in Fig 4) can be seen closer to theproduct of the source brand in 7 stages of 1 (looks different) to 7 (looks source brand). An weighted
averages of these results are shown in the Table. 1. We have shown that our output is close enough tothe learning source brand, as the output costumes are significantly closer to the learning source brandthan other brand costumes. Looking at the answers of the subjects, the clothes that were judged to be
most similar to the clothes of the original brand were the output of our system (3) and the clothes of
the actual learning source brand.
2

https://page.auctions.yahoo.co.jp/jp/auction/v499221070, https://page.auctions.yahoo.co.jp/jp/auction/260446858,
https://page.auctions.yahoo.co.jp/jp/auction/m218550387

2

References[1] Alec R. & Luke M. & Soumith C. (2015) Unsupervised representation learning with deep convolutional
generative adversarial networks., arXiv preprint arXiv:1511.06434[2] FASHION PRESS. (2017) Retrieved September 15, 2017 from www.fashionpress.net/collections/brand/42

A

Supplementary materials

Figure 2: Generated images. The above image is clothes images (128 px). The below image is certain
brand shoe images that we additionally genrated (256 px).

3

A

(1)

(5)

(2)

(6)

(3)

(7)

(4)

B

(1)

(5)

(2)

(6)

(3)

(7)

(4)

Figure 3: Drawn patterns.

(1)

(5)

(2)

(6)

(3)

(7)

(4)

(8)

(9)

Figure 4: We showed these clothes to subjects to survey can they distinguish brands.

4