SketchINR: A First Look into Sketches as Implicit Neural Representations
<|reference_start|>SketchINR: A First Look into Sketches as Implicit Neural Representations: 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.<|reference_end|>
@article{bandyopadhyay2024sketchinr:,
    title={SketchINR: A First Look into Sketches as Implicit Neural Representations},
    author={Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury,
  Aneeshan Sain, Tao Xiang, Timothy Hospedales, Yi-Zhe Song},
    journal={arXiv preprint arXiv:2403.09344},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09344},
    primaryClass={cs.CV cs.AI}
}