In this paper, we present a novel system for Human-Robot Interactive Creation of Artworks (HRICA). Different from previous robot painters, HRICA allows a human user and a robot to alternately draw strokes on a canvas, to collaboratively create a portrait drawing through frequent interactions. The key is to enable the robot to understand human intentions, during the interactive creation process. We here formulate this as a mask-free image inpainting problem, and propose a novel method to estimate the complete version of a portrait drawing, after the human user has drawn some initial strokes. In this way, the robot can select some complementary strokes and draw them on the canvas. To train and evaluate our inpainting method, we construct a novel large-scale portrait drawing dataset, CelebLine, which composes of high-quality portrait line-drawings, with dense labels of both 2D semantic parsing masks and 3D depth maps. Finally, we develop a human-robot interactive drawing system with low-cost hardware, user-friendly interface, and interesting creation experience. Experiments show that our robot can stably cooperate with human users to create diverse styles of portrait drawings. In addition, our portrait drawing inpainting method significantly outperforms previous advanced methods. The code and dataset have been released at: https://github.com/fei-aiart/HRICA.

In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. Our main idea is bridging the photo domain ${\mathcal{X}}$ and the sketch domain Y by using the line-drawing domain ${\mathcal{Z}}$. Specially, we map both photos and sketches to line-drawings by using a neural style transfer method, i.e. $F:{\mathcal{X}}/{\mathcal{Y}} \mapsto {\mathcal{Z}}$. Consequently, we obtain pseudo paired data $({\mathcal{Z}},{\mathcal{Y}})$, and can learn the mapping $G:{\mathcal{Z}} \mapsto {\mathcal{Y}}$ in a supervised learning manner. In the inference stage, given a facial photo, we can first transfer it to a line-drawing and then to a sketch by G ○ F. Additionally, we propose a novel stroke loss for generating different types of strokes. Our method, termed sRender, accords well with human artists’ rendering process. Experimental results demonstrate that sRender can generate multi-style sketches, and significantly outperforms existing unpaired image-to-image translation methods.