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Introduction

Generative models have revolutionized the field of computer graphics by enabling the synthesis of photorealistic images. These models have the capability to generate images that closely resemble real photographs, providing a powerful tool for artists, designers, and developers. In recent years, deep learning techniques have been at the forefront of advances in generative models, allowing for the creation of highly realistic images with unprecedented levels of detail and realism.

This thesis aims to explore the use of generative models for photorealistic image synthesis in computer graphics. By employing state-of-the-art deep learning techniques, we seek to improve the quality and efficiency of image synthesis, with a focus on creating images that are indistinguishable from real photographs. This research has the potential to impact a wide range of applications, from virtual reality and gaming to film production and digital art.

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 Introduction to Generative Models
2.2 Evolution of Generative Models in Computer Graphics
2.3 Deep Learning Techniques for Image Synthesis
2.4 Photorealistic Image Generation Methods
2.5 Applications of Generative Models in Computer Graphics
2.6 Challenges and Limitations of Current Approaches
2.7 Advances in Generative Adversarial Networks (GANs)
2.8 Conditional Generative Models
2.9 Image-to-Image Translation Techniques
2.10 Evaluation Metrics for Image Synthesis

Chapter 3: System Design and Methodology

3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Model Selection and Training
3.4 Loss Functions and Optimization
3.5 Hyperparameter Tuning
3.6 Data Augmentation Techniques
3.7 Model Evaluation and Validation
3.8 Performance Metrics
3.9 Ethical Considerations

Chapter 4: System Implementation

4.1 Implementation Environment
4.2 Code Development and Debugging
4.3 Training Data Selection
4.4 Model Training and Fine-Tuning
4.5 Computational Resources
4.6 Performance Optimization
4.7 Results Analysis
4.8 Comparison with Existing Methods

Chapter 5: Conclusion and Summary

5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Future Research
5.4 Practical Applications
5.5 Limitations and Challenges
5.6 Concluding Remarks

Thesis Overview on Generative Models for Photorealistic Image Synthesis in Computer Graphics

Generative models have emerged as a powerful tool for photorealistic image synthesis in computer graphics, enabling the creation of highly realistic images that are indistinguishable from real photographs. This thesis investigates the use of deep learning techniques to improve the quality and efficiency of image synthesis, with a focus on creating images with unparalleled levels of detail and realism.

Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive literature review on generative models, deep learning techniques, image synthesis methods, applications, challenges, and evaluation metrics.

Chapter 3 delves into the system design and methodology, covering system architecture, data collection, model selection, training, optimization, hyperparameter tuning, evaluation, and ethical considerations. Chapter 4 details the system implementation, including the implementation environment, code development, training data selection, model training, performance optimization, and results analysis.

Chapter 5 concludes the thesis with a summary of findings, contributions, implications for future research, practical applications, limitations, and concluding remarks. This research aims to advance the field of computer graphics through the development of cutting-edge generative models for photorealistic image synthesis.

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