In the last few years, Neural Painting (NP) techniques became capable of producing extremely realistic artworks. This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP. Considering a setting where a user looks at a scene and tries to reproduce it on a painting, our objective is to develop a computational framework to assist the users creativity by suggesting the next strokes to paint, that can be possibly used to complete the artwork. To accomplish such a task, we propose I-Paint, a novel method based on a conditional transformer Variational AutoEncoder (VAE) architecture with a two-stage decoder. To evaluate the proposed approach and stimulate research in this area, we also introduce two novel datasets. Our experiments show that our approach provides good stroke suggestions and compares favorably to the state of the art. Additional details, code and examples are available at https://helia95.github.io/inp-website.

通过提出一种基于条件变换自动编码器 (VAE) 架构的双阶段解码器的创新方法 I-Paint，本研究旨在为用户在绘画过程中提供下一笔建议，以帮助提高创造力，并且通过引入两个新的数据集进行评估和刺激相关领域的研究。实验结果表明该方法提供良好的笔划建议，并与现有技术相比表现出优势。

交互式神经绘画