2024/03/28 by Noah Pursell, Anindya Maiti, Pursell, Noah +1
Computer Science · Engineering · #3D Shape Modeling and Analysis #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image Processing and 3D Reconstruction #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.18247
openalex publication_date 2024/03/28 · openalex created_date 2024/05/30 · openalex updated_date 2026/07/28
We present a novel framework to advance generative artificial intelligence (AI) applications in the realm of printed art products, specifically addressing large-format products that require high-resolution artworks. The framework consists of a pipeline that addresses two major challenges in the domain: the high complexity of generating effective prompts, and the low native resolution of images produced by diffusion models. By integrating AI-enhanced prompt generations with AI-powered upscaling techniques, our framework can efficiently produce high-quality, diverse artistic images suitable for many new commercial use cases. Our work represents a significant step towards democratizing high-quality AI art, opening new avenues for consumers, artists, designers, and businesses.