Differentiable Drawing and Sketching
This provides a foundational tool for machine learning applications in graphics and vision, though it is incremental in building on distance transform concepts.
The paper tackles the problem of making drawing and sketching operations differentiable for end-to-end learning, enabling tasks like generating sketches from photographs and unsupervised vectorization of handwritten digits.
We present a bottom-up differentiable relaxation of the process of drawing points, lines and curves into a pixel raster. Our approach arises from the observation that rasterising a pixel in an image given parameters of a primitive can be reformulated in terms of the primitive's distance transform, and then relaxed to allow the primitive's parameters to be learned. This relaxation allows end-to-end differentiable programs and deep networks to be learned and optimised and provides several building blocks that allow control over how a compositional drawing process is modelled. We emphasise the bottom-up nature of our proposed approach, which allows for drawing operations to be composed in ways that can mimic the physical reality of drawing rather than being tied to, for example, approaches in modern computer graphics. With the proposed approach we demonstrate how sketches can be generated by directly optimising against photographs and how auto-encoders can be built to transform rasterised handwritten digits into vectors without supervision. Extensive experimental results highlight the power of this approach under different modelling assumptions for drawing tasks.