In this paper, we present a method for converting a given scene image into a sketch using different types and multiple levels of abstraction. We distinguish between two types of abstraction. The first considers the fidelity of the sketch, varying its representation from a more precise portrayal of the input to a looser depiction. The second is defined by the visual simplicity of the sketch, moving from a detailed depiction to a sparse sketch. Using an explicit disentanglement into two abstraction axes -- and multiple levels for each one -- provides users additional control over selecting the desired sketch based on their personal goals and preferences. To form a sketch at a given level of fidelity and simplification, we train two MLP networks. The first network learns the desired placement of strokes, while the second network learns to gradually remove strokes from the sketch without harming its recognizability and semantics. Our approach is able to generate sketches of complex scenes including those with complex backgrounds (e.g., natural and urban settings) and subjects (e.g., animals and people) while depicting gradual abstractions of the input scene in terms of fidelity and simplicity.

翻译：本文提出了一种方法，可将给定场景图像转换为具有不同类型与多层级抽象的草图。我们区分了两种抽象类型：第一种考虑草图的保真度，其表示从对输入的精准刻画过渡到松散描绘；第二种由草图的视觉简洁性定义，从细节丰富的描绘过渡到稀疏草图。通过将抽象显式解耦为两个维度——并为每个维度设置多个层级——用户能够依据个人目标与偏好更灵活地选择所需草图。为生成特定保真度与简化层级的草图，我们训练了两个多层感知机网络：第一个网络学习笔触的期望放置位置，第二个网络学习在不损害草图可识别性与语义的前提下逐步移除笔触。本方法可生成复杂场景的草图（包括具有复杂背景（如自然与城市环境）及复杂主体（如动物与人物）的场景），同时实现输入场景在保真度与简洁性维度的渐进式抽象表达。