FashionGAN: Difference between revisions

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= Deliverables =
= Deliverables =
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= Milestones and Project plan =
= Milestones and Project plan =

Revision as of 12:43, 14 December 2023

Introduction

Je me suis mis en groupe avec Romane, erreur.

Motivation

The impetus behind embarking on this groundbreaking project stemmed from a collective desire to revolutionize the creative process for fashion designers. Recognizing the ever-evolving landscape of the fashion industry, our team felt compelled to innovate and address a common challenge: the need for novel inspiration and streamlined design techniques.

The foundation of our motivation lies in leveraging the vast potential of artificial intelligence alongside an extensive repository of fashion show data. The aim? To empower designers with a cutting-edge tool that transcends conventional boundaries. By harnessing AI capabilities, we envisioned a tool that not only generates fresh and inventive clothing designs but also acts as a wellspring of inspiration for designers seeking a new creative direction.

The core essence of our project centers on the creation of an AI-driven platform capable of producing unique clothing visuals. Through the amalgamation of innovative technologies like DragGAN and AutoEncoder, designers will not only access generated designs but also possess the ability to customize and refine these creations to match their artistic vision.

Ultimately, our aspiration is to provide fashion designers with an invaluable resource that nurtures creativity, encourages experimentation, and accelerates the design process. By enabling designers to explore limitless possibilities, our tool aspires to be a catalyst for transformative innovation within the fashion sphere.

Deliverables

Milestones and Project plan

Milestones

Milestone 1: DragGAN

  • Understand how DragGAN works
  • Find a dataset that would be appropriate for our utilization
  • Train StyleGAN

Milestone 2: Texture swap

  • Find a way to apply a texture change on an image
  • Train the Swapping Autoencoder for Deep Image Manipulation
  • Implement the Texture swap interface in our project


Milestone 3: User Interface

  • Change DragGAN's interface to make it more intuitive for our project

Milestone 4: Deliverables

  • Deliver the code on Github
  • Write the wiki page
  • Prepare the presentations

Project Plan

Week Tasks Completion
Week 3
  • Choose our project between the ones that were presented in class
  • Prepare for the first presentation
Weeks 4-5-6
  • Find some clothing datasets
  • Read papers, understand how DragGAN works
  • Define the project
  • Get familiar with the libraries we want to use (selenium, opencv...)
Weeks 7-8-9
  • Scrap the data from nowfashion website
  • Resize images
  • Clean dataset (remove the pictures that aren't in the same format as the other)
  • Get familiar with StyleGAN and its functioning
  • Find a textile dataset
Week 10
  • Define a precise planning for the following weeks
  • Prepare for the presentation
  • Write the Project Plan and the Milestones on the wiki page
  • Figure out how to implement the texture changes
  • Begin the training of the dataset with StyleGAN
Week 11
  • Install a Virtual Machine to use linux
  • Finish the training on the VM
  • Get DragGAN to work on the VM
Week 12
  • Work on the texture changing tool
  • Implement the texture changing interface
  • Start writing the description of the methods on the wiki page
Week 13
  • Adapt the DragGAN GUI to be more intuitive
  • Finalize and clean the code to then commit it to Github
  • Finish writing the wiki page