Physically Embodied Deep Image Optimisation
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Abstract (arXiv)
Physical sketches are created by learning programs to control a drawing robot. A differentiable rasteriser is used to optimise sets of drawing strokes to match an input image, using deep networks to provide an encoding for which we can compute a loss. The optimised drawing primitives can then be translated into G-code commands which command a robot to draw the image using drawing instruments such as pens and pencils on a physical support medium.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 20 Jan 2022.
Builds on
6 of 9 references.
References · 6
- 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
- 2015A Neural Algorithm of Artistic Stylecited by 480
- 2017CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Normscited by 96
- 2018Synthesizing Programs for Images using Reinforced Adversarial Learningcited by 72
- 2019Neural Painters: A learned differentiable constraint for generating brushstroke paintingscited by 28
- 2021Differentiable Drawing and Sketchingcited by 20
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