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This work presents CLIPDraw, an algorithm that synthesizes novel drawingsbased on natural language input. CLIPDraw does not require any training; rathera pre-trained CLIP language-image encoder is used as a metric for maximizingsimilarity between the given description and a generated drawing. Crucially,CLIPDraw operates over vector strokes rather than pixel images, a constraintthat biases drawings towards simpler human-recognizable shapes. Results comparebetween CLIPDraw and other synthesis-through-optimization methods, as well ashighlight various interesting behaviors of CLIPDraw, such as satisfyingambiguous text in multiple ways, reliably producing drawings in diverseartistic styles, and scaling from simple to complex visual representations asstroke count is increased. Code for experimenting with the method is availableat:https://colab.research.google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb

CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders