arXiv:2106.14843cs.CV
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders
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Abstract
This work presents CLIPDraw, an algorithm that synthesizes novel drawings based on natural language input. CLIPDraw does not require any training; rather a pre-trained CLIP language-image encoder is used as a metric for maximizing similarity between the given description and a generated drawing. Crucially, CLIPDraw operates over vector strokes rather than pixel images, a constraint that biases drawings towards simpler human-recognizable shapes. Results compare between CLIPDraw and other synthesis-through-optimization methods, as well as highlight various interesting behaviors of CLIPDraw, such as satisfying ambiguous text in multiple ways, reliably producing drawings in diverse artistic styles, and scaling from simple to complex visual representations as stroke count is increased. Code for experimenting with the method is available at: https://colab.research.google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb
I made some image generation with CLIP and Evolutionary algorithms not so long ago[1] and the results had a bit more life than I was expecting. It is still a contested area of research and you have some cool stuff like CLIPDraw[2] where they use gradient decent to approximate a…
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