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.

```
@inproceedings{bandyopadhyay-inr,
title={{SketchINR : A First Look into Sketches as Implicit Neural Representations}},
author={Bandyopadhyay, Hmrishav and Bhunia, Ayan Kumar and Chowdhury, Pinaki Nath and Sain, Aneeshan and Xiang, Tao and Hospedales, Timothy and Song, Yi-Zhe},
booktitle={CVPR},
year={2024}
}
```