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.

翻译：为弥合艺术家与非专业人士之间的鸿沟，本文提出统一框架Neural-Polyptych，通过将交互式手绘草图与原始画作片段无缝融合，促进宏大高分辨率绘画的创作。我们设计了基于多尺度GAN的架构，将生成过程分解为全局特征识别与局部特征识别两个部分。为提升用户草图轮廓生成语义细节的保真度，我们引入基于参考库策略的对应注意力模块，确保在艺术作品中生成高质量、细节丰富的元素。最终结果通过精细融合这些局部元素并保持连贯的全局一致性来实现。该方法因此能够生成百万像素级的数字绘画，兼容多样化的艺术表现形式，并允许用户以可控方式进行内容再创作。我们在东西方不同绘画流派中验证了本方法的有效性。大型绘画扩展、纹理重组、流派转换、壁画修复与画面重构等应用均可基于本框架成功实现。