Interactive Neural Painting
This work addresses the need for interactive tools in neural painting to enhance user creativity, though it is incremental as it builds on existing neural painting techniques.
The paper tackles the problem of interactive neural painting by proposing I-Paint, a method that suggests next strokes to assist users in reproducing scenes, and introduces two new datasets, showing it provides good suggestions and compares favorably to state-of-the-art techniques.
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.