Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings. Abstraction entails identifying the essential visual properties of an object or scene, which requires semantic understanding and prior knowledge of high-level concepts. Abstract depictions are therefore challenging for artists, and even more so for machines. We present CLIPasso, an object sketching method that can achieve different levels of abstraction, guided by geometric and semantic simplifications. While sketch generation methods often rely on explicit sketch datasets for training, we utilize the remarkable ability of CLIP (Contrastive-Language-Image-Pretraining) to distill semantic concepts from sketches and images alike. We define a sketch as a set of B\'ezier curves and use a differentiable rasterizer to optimize the parameters of the curves directly with respect to a CLIP-based perceptual loss. The abstraction degree is controlled by varying the number of strokes. The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual components of the subject drawn.

翻译：抽象是素描的核心,因为线条图画的简单和最低限度性质。 抽象意味着要确定一个对象或场景的基本视觉特性,这需要语义上的理解和对高级概念的事先知识。 因此,抽象描述对艺术家来说具有挑战性,对机器来说更具有挑战性。 我们展示了CLIPasso, 一种在几何和语义简化的指引下可以实现不同程度的抽象化的物体素描方法。 素描生成方法通常依靠明确的素描数据集进行培训,但我们利用CLIP(Ctrastem-Language-Image-Pretrainction)的非凡能力来从素描和图像中提取语义概念。 我们把素描定义为一套B\'ezier曲线,并使用一种不同的光化器,直接优化曲线的参数,与基于CLIP的感知性损失直接相关。 抽象度由不同的中线数控制。 我们生成的素描图在保持主题的可识别性、基本结构和基本视觉组成部分的同时展示多种程度。