GAN-Based Visual Aware Interactive Fashion Design

1. Generative Adversarial Network-based Visual Aware Interactive
Fashion Design Framework
By Ashenafi Workie
Advisor :Prof Yun koo Chung(PhD.)

2. Presentation Outline
 Introduction
 Statement of problem
 Objective
 Scope, constraints & contribution
 Related works
 Theoretical & conceptual framework
 Proposed Methods
 Thesis budget and schedule timeline
 Conclusion
2

3. 33
Domains in the fashion design requires
 Effective interactive Design
Interactive design may applied in
 Clothing and painting art
 Building and home utility products
This can be achieve using generative model approach giving user given
input for product category
Introduction

4. 44
Generating conditioned image
 Color
 shape
 style
 Pattern
 texture etc.)
Improve some nose and artifacts
Pull to public cloth dataset
Motivation

5. 55
 challenging task
hag subjectivity
Focus on designer interest
(no more creativity) and Difficult to
share style
Difficulties in training and produce
high-resolution.[2]
Statement of problem

6. 66
Here are research question
 REQ1: What are the effective ways to interactive fashion design?
 REQ2: What kind proposed solution to be used for generation of image for
better interactive design?
 REQ3: How the quality of generated fashion design can be improved and
comparison of results based evaluation metrics?
Research Question

7. 77
Design and develop visual aware interactive fashion design framework
through deep generative network to improve quality and fine-grained style.
General Objective

8. 88
Specific Objective
Here are list of specific objective
Investigate the domain using Systematic Literature review
Collect dataset from different source
Prepare annotation and preprocessing the data
Create a fusion CGAN and SRGAN
Train the model and feed the data
Present the result and evaluate model

9. 99
Scope
Here are list of scope of research
Collecting and pre-processing the dataset
Creating a fusion of conditional GAN ,where it easy for controlling and
SRGAN to enhance the quality of generated image
Present result and evaluation result
Constraints
No sufficient dataset available
lack of high computational resource(GPU) .

10. 1010
[2] Kang et.al Visually-Aware Fashion Recommendation and Design with
Generative Image Models.
Methods :GAN
 Objective : suggesting existing items on fashion images for both prediction
and design
 Gap(future research) :
 Poor quality and
 difficult control fine-grained style
Related works

11. 1111
Related works
[5] Y. R et.al (2018) FashionGAN: Display your fashion design using
Conditional Generative Adversarial Nets
Methods :FashionGAN
Objective :Automatically generated fashion image and establishes a
bijection relationship between generated image .
Gap(future research) :
 workable for single-color and regular
it cannot map an irregular one with multiple colors and style
Needed further research

12. 1212
Related works
Author and Title Methodology Objective GAP(future work)
[1] Yildirim et.al (2019)
Generating High-
Resolution Fashion Model
Images Wearing Custom
Outfits
 unconditional
 Conditional
GAN
 Transfer the
style/color
 generate images
of fashion models
• Quality is not good
and consistent
model
[3] Christoph et. Al A
Generative Model of
People in Clothing
• graphics
pipeline
design and
• 3D data
acquisition
 Semantic
segmentation
prediction
 texture prediction
• High
computational
resources for
rendering

13. 1313
Related works cont...d
Author and Title Methodology Objective GAP(future work)
[4] Zhu et.al (2017)
Be Your Own Prada:
Fashion Synthesis with
Structural Coherence
 semantic
segmentation
map.
 compositional
mapping layer
collecting
sentence
• model
can also render textured
background .
• Do not post
processing of
the background
[6] Pandey
et al (2019)Poly-gan:
Multi-conditioned Gan
For Fashion Synthesis
• Poly-GAN  multiple inputs and is
suitable for many
tasks, image
alignment, image
stitching and
inpainting
Design
architecture to
apply for a variety
of applications.

14. 1414
Theoretical framework of GAN
CGAN is very similar to GAN, except both
the generator and discriminator are
conditioned on some extra information, y.
Gan’s have two networks
1.Generator G takes some random
noise and tries to produce real input
training images by fooling the
discriminator D.
2.Discriminator D tries to classify the
input being real if it’s coming from the
training data and fake if it’s coming
from the generator G.

15. 1515
Methodology
Dataset
 Scrapping website like pintrest , esy.com
 Download from channel and page
 Request image from fashion studio
 Capture image
Preprocessing
Augmentation, Scaling , background
Feature extraction
 GaitGAN:[34] is used to reduce the effect
of variations
 PCA-> to reduce the dimension
1
PreprocessingDataset
2
Feature Ex and Red
3 4
Train for
CGAN
Train for
SRGAN
Present
result
5 6

16. 1616
LR image
HR image
generator
High quality fake or real
Dataset
Proposed architecture workflow
Xr
Y
Y
Z
Generator
Discriminator
Generator
Discriminator
D
D
A Generator(An artist) neural network.
•Discriminator has to
correctly label real images
which are coming from
training data set as “real”.

17. 1717
Cont...d
CGAN for generating condition fashion images
SRGAN to Refine the quality from generated one
Nobility
 Enhancement of Quality image
 control fine-grained style
 Improve to work with multiple
color

18. 1818
Research tools
Software tools
Tensor-flow
Keras
MS office
Mendeley desktop
 Colab
Hardware tools
 GPU
RAM
SSD
Camera

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Contribution and application of result
Collecting dataset and pull to github
Creating of fusion algorithm CGAN and SRGAN
Enhance quality generated image(SRGAN) and Control fine grained style
(CGAN)
Application of result
Style transfer across multiple domain image generation
Image to image translation :learn mapping input and output
photo editing and image enhancement

20. 2020
Budget plan (operation cost)
Operation cost
Item No Item Name Unit price Quantity * unit Total Source
1 Data collection 2000 3*2000 6000
Coveredby
Researchfund
2 Data preparation 1500 3*1500 4500
3 Print cost and binding 650 4*650 2600
3 Transport cost 300 6*300 1800
5 Contingency plan (10%) 10% 1*2500 2500
Total operation cost 14700
Total material cost 7800
Total cost operation +material _contingency =25,000 ETB
Material cost
Item no Item name Specification Unit price Qty. Total
1 GPU MSI RTX 2070 250$ 1 -
2 Flash USB SanDisk 32GB 10$ 1 -
Total 210 USD =7800 ETB

21. 2121
July 2019 days Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
Literature review 334
Writing Proposal 99
Proposal defense -
Data collection 120
Data preparation 50
Train model 60
Verify the model 90
Result Report and
summarization
90
TodayTodayCompleteComplete IncompleteIncomplete Not StartedNot Started
2020
June 2020
Months
Research timeline and schedule

22. (+251) 919-283004
(+251) 933-880022
Ashumare.com
Ashenafiworkie@gmail.com
Thank You
By Ashenafi Workie