Estimation of Global Illumination using Cycle-Consistent Adversarial Networks

The field of computer graphics has made significant progress over the years, transforming from simple, pixelated images to highly realistic visuals used across various industries including entertainment, fashion, and video gaming. However, the traditional process of rendering images remains complex and time-consuming, requiring a deep understanding of geometry, materials, and textures. This thesis introduces a simpler approach through a machine learning model, specifically using Cycle-Consistent Adversarial Networks (CycleGAN), to generate realistic images and estimate global illumination in real-time, significantly reducing the need for extensive expertise and time investment. Our experiments on the Blender and Portal datasets demonstrate the model's ability to efficiently generate high-quality, globally illuminated scenes, while a comparative study with the Pix2Pix model highlights our approach's strengths in preserving fine visual details. Despite these advancements, we acknowledge the limitations posed by hardware constraints and dataset diversity, pointing towards areas for future improvement and exploration. This work aims to simplify the complex world of computer graphics, making it more accessible and user-friendly, while maintaining high standards of visual realism.