Abstract
We propose SketchINR, to advance the representation of vector sketches with
implicit neural models. A variable length vector sketch is compressed into a
latent space of fixed dimension that implicitly encodes the underlying shape as
a function of time and strokes. The learned function predicts the $xy$ point
coordinates in a sketch at each time and stroke. Despite its simplicity,
SketchINR outperforms existing representations at multiple tasks: (i) Encoding
an entire sketch dataset into a fixed size latent vector, SketchINR gives
$60\times$ and $10\times$ data compression over raster and vector sketches,
respectively. (ii) SketchINR's auto-decoder provides a much higher-fidelity
representation than other learned vector sketch representations, and is
uniquely able to scale to complex vector sketches such as FS-COCO. (iii)
SketchINR supports parallelisation that can decode/render $\sim$$100\times$
faster than other learned vector representations such as SketchRNN. (iv)
SketchINR, for the first time, emulates the human ability to reproduce a sketch
with varying abstraction in terms of number and complexity of strokes. As a
first look at implicit sketches, SketchINR's compact high-fidelity
representation will support future work in modelling long and complex sketches.