CLIPasso: Semantically-Aware Object Sketching

Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo, Roman Christian Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, Ariel Shamir

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

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 Bézier 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.

Original languageEnglish
Article number3530068
JournalACM Transactions on Graphics
Volume41
Issue number4
DOIs
StatePublished - 22 Jul 2022

Funding

FundersFunder number
Deutsch Foundation
Yandex Initiative in Machine Learning
Israel Science Foundation2492/20, 3441/21

    Keywords

    • Image-based rendering
    • Sketch synthesis
    • Vector line art generation

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