SketchINR: A First Look into Sketches as Implicit Neural Representations
CVPR 2024(2024)
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× and 10× 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 ∼100×
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.
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