Neural-Polyptych: Content Controllable Painting Recreation for Diverse Genres
This work addresses the problem of enabling non-specialists to create large, high-quality digital paintings, offering a tool for artistic expression and various applications like mural art restoration.
This paper introduces Neural-Polyptych, a framework that allows non-specialists to create high-resolution digital paintings by combining hand-drawn sketches with fragments of original paintings. It uses a multi-scale GAN and a Correspondence Attention module with a Reference Bank strategy to generate detailed elements and blend them into megapixel-scale artworks.
To bridge the gap between artists and non-specialists, we present a unified framework, Neural-Polyptych, to facilitate the creation of expansive, high-resolution paintings by seamlessly incorporating interactive hand-drawn sketches with fragments from original paintings. We have designed a multi-scale GAN-based architecture to decompose the generation process into two parts, each responsible for identifying global and local features. To enhance the fidelity of semantic details generated from users' sketched outlines, we introduce a Correspondence Attention module utilizing our Reference Bank strategy. This ensures the creation of high-quality, intricately detailed elements within the artwork. The final result is achieved by carefully blending these local elements while preserving coherent global consistency. Consequently, this methodology enables the production of digital paintings at megapixel scale, accommodating diverse artistic expressions and enabling users to recreate content in a controlled manner. We validate our approach to diverse genres of both Eastern and Western paintings. Applications such as large painting extension, texture shuffling, genre switching, mural art restoration, and recomposition can be successfully based on our framework.