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The drawing process is crucial to understanding the final result of a drawing. There has been a long history of understanding human drawing; what kinds of strokes people use and where they are placed. An area of interest in Artificial Intelligence is developing systems that simulate human behavior in drawing. However, there has been little work done to understand the order of strokes in the drawing process. Without sufficient understanding of natural drawing order, it is difficult to build models that can generate natural drawing processes. In this paper, we present a study comparing multiple types of stroke orders to confirm findings from previous work and demonstrate that multiple orderings of the same set of strokes can be perceived as human-drawn and different stroke order types achieve different perceived naturalness depending on the type of image prompt. 

Most non-photorealistic rendering (NPR) methods for line drawing synthesis operate on a static shape. They are not tailored to process animated 3D models due to extensive per-frame parameter tuning needed to achieve the intended look and natural transition. This paper introduces a framework for interactive line drawing synthesis from animated 3D models based on a learned style space for drawing representation and interpolation. We refer to style as the relationship between stroke placement in a line drawing and its corresponding geometric properties. Starting from a given sequence of an animated 3D character, a user creates drawings for a set of keyframes. Our system embeds the raster drawings into a latent style space after they are disentangled from the underlying geometry. By traversing the latent space, our system enables a smooth transition between the input keyframes. The user may also edit, add, or remove the keyframes interactively, similar to a typical keyframe-based workflow. We implement our system with deep neural networks trained on synthetic line drawings produced by a combination of NPR methods. Our drawing-specific supervision and optimization-based embedding mechanism allow generalization from NPR line drawings to user-created drawings during run time. Experiments show that our approach generates high-quality line drawing animations while allowing interactive control of the drawing style across frames.