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Introduction:

Generative adversarial networks (GANs) have emerged as a powerful tool in the field of computer vision and image processing. One of the exciting applications of GANs is in virtual try-on systems, where users can try on clothing virtually before making a purchase. This technology has the potential to revolutionize the online shopping experience by providing users with a more realistic and personalized shopping experience.

Background of study:

With the rise of e-commerce, virtual try-on applications have become increasingly popular among retailers and consumers. These applications use computer vision techniques to generate realistic images of users wearing different clothing items. GANs have shown great promise in improving the realism and accuracy of these virtual try-on systems.

Problem Statement:

Despite the progress made in virtual try-on applications, there are still challenges that need to be addressed. One of the main challenges is generating high-quality and realistic images of users wearing clothing items in different poses and lighting conditions. This requires sophisticated algorithms and techniques that leverage the power of GANs.

Objective of study:

The main objective of this study is to develop a GAN-based virtual try-on system that can generate realistic images of users wearing clothing items. This system will incorporate state-of-the-art techniques in computer vision and image processing to improve the accuracy and realism of virtual try-on applications.

Limitation of study:

While GANs have shown great potential in virtual try-on applications, there are limitations to consider. These include issues related to data privacy, computational complexity, and the need for large amounts of training data. This study will explore these limitations and propose solutions to address them.

Scope of study:

This study will focus on the development and evaluation of a GAN-based virtual try-on system for clothing items. The system will be tested using a dataset of clothing images and user photos to evaluate its performance and accuracy. The study will also explore the impact of different GAN architectures and training techniques on the realism of the generated images.

Significance of study:

The development of a GAN-based virtual try-on system has the potential to revolutionize the way we shop online. By providing users with a more realistic and personalized shopping experience, virtual try-on applications can help retailers increase sales and reduce returns. This study will contribute to the advancement of virtual try-on technology and its applications in the fashion industry.

Structure of the Thesis:

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of GANs
2.2 Virtual try-on applications
2.3 State-of-the-art techniques in virtual try-on
2.4 Challenges in virtual try-on applications
2.5 Advances in GAN-based virtual try-on systems
2.6 Evaluation metrics for virtual try-on applications
2.7 Data privacy considerations in virtual try-on systems
2.8 Computational complexity in GAN-based systems
2.9 Training data requirements for GANs
2.10 Future directions in virtual try-on research

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data collection and preprocessing
3.3 GAN training process
3.4 Image generation techniques
3.5 Evaluation methodology
3.6 Performance metrics
3.7 User interface design
3.8 Implementation details
3.9 Experimental setup

Chapter 4: System Implementation
4.1 GAN architecture selection
4.2 Training data selection
4.3 Hyperparameter tuning
4.4 Model optimization
4.5 Performance evaluation
4.6 User testing
4.7 System refinements
4.8 Results analysis

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Limitations and future work
5.4 Conclusion
5.5 Recommendations for future research

Thesis Overview:

Virtual try-on applications have become increasingly popular in the fashion industry, allowing users to try on clothing items virtually before making a purchase. In this thesis, we focus on the development of a GAN-based virtual try-on system to improve the realism and accuracy of virtual try-on applications. The study will explore the challenges and limitations of existing virtual try-on systems, propose solutions to address them, and evaluate the performance of the GAN-based system using a dataset of clothing images and user photos. By developing a more realistic and personalized virtual try-on system, we aim to enhance the online shopping experience and provide retailers with a valuable tool to increase sales and reduce returns. Through this research, we hope to contribute to the advancement of virtual try-on technology and its applications in the fashion industry.

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