Generative Adversarial Network Art Generator for Sculpture Analysis

This study examines computer-generated sculpture using a hybrid architecture of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). CNNs are essential for visual data analysis and processing, while GANs iteratively blur the line between synthesis and authenticity. A dynamically interacting generator and discriminator network form the GAN framework. The discriminator, trained to distinguish authentic sculptures from computer-generated ones, guides the generator toward greater realism and complexity with each iteration. This study analyzes the complicated dynamics of GAN-enabled sculpture production, examining several elements that affect the result. We intend to illuminate the complex relationship between composition, texture, and shape of digitally generated sculptures by analyzing sculptures using GANs and CNNs. We want to understand their formation methods as well as their aesthetic value. We also consider how GAN-generated sculptures may be transformed beyond traditional artistic usage. The sculptures offer unique opportunities for artistic expression, scholarly study, and practical use. They can enhance art, education, architectural design, and historical preservation. This thorough inquiry using GANs and CNNs aims to push the limitations of traditional art creation and explore unknown sculpture interpretation and analysis zones.