Construction of Artistic Intelligence System based on Multi-View Image Generation Algorithm

This paper explores the development of an artistic intelligence system through the use of a multi-view image generation algorithm, demonstrating its versatility in e-commerce, virtual reality, medical imaging, security surveillance, and autonomous driving. An in-depth study of state-of-the-art algorithms, such as Pix2Vox and conditional generative networks, contributes to the development of 3D structure reassembly and viewpoint synthesis in computer vision and machine learning. The proposed methodology combines multi-view image analysis and a Generative Adversarial Network (GAN)-based image generation algorithm to improve the accuracy and sophistication of image generation. The artistic intelligence system provides computational integration for efficient design in industrial crafts, enabling rapid realization of patterns or 3D models by inputting design requirements and configuring parameters. The system's ability to store and leverage existing data streamlines design processes, increasing operational efficiency and adaptability. It facilitates the classification of stored data, creating a personalized design repository. Experimental validation proves the effectiveness of the system. Expert evaluations on realism, diverse image production metrics, and object recognition accuracy demonstrate the system's ability to generate realistic and diverse artistic content with accurate object identification. The study concludes by highlighting the potential impact of the proposed system on industrial craftsmanship and its role in fostering innovative intersections between art and artificial intelligence