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.
Abstract (translated)
我们提出了SketchINR，以通过隐式神经模型提高向量草图的表示。一个长度可变的向量草图被压缩成一个固定维度的潜在空间，其中隐含地编码了 underlying 形状作为时间和水流的函数。学习到的函数在每个时间和线条上预测草图中的 $xy$ 点坐标。尽管它的简单性，SketchINR在多个任务上优于现有的表示： (i) 将整个草图数据集压缩到固定大小的潜在向量中，SketchINR在遥感草图和向量草图上分别给出了60倍和10倍的数据压缩。 (ii) SketchINR的自动编码器提供了比其他学习到的向量草图表示更高的保真度，并且具有独特的能力将其扩展到复杂的向量草图（如FS-COCO）。 (iii) SketchINR支持并行化，这使得它能够比其他学习到的向量表示更快地解码/渲染大约100倍的草图。 (iv) SketchINR是第一个模拟人类能力在抽象程度和复杂性方面复制草图的。作为对隐性草图的第一印象，SketchINR的紧凑高保真度表示将支持未来在建模长和复杂草图方面的研究。
URL
https://arxiv.org/abs/2403.09344
