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.

翻译：摘要：我们提出SketchINR，旨在利用隐式神经模型推进矢量草图的表示。该方法将可变长度的矢量草图压缩为固定维度的潜在空间，该空间以时间与笔划的函数形式隐式编码底层形状。所学习的函数可预测草图中每个时间点与笔划的$xy$点坐标。尽管结构简洁，SketchINR在多项任务上优于现有表示方法：(i) 将整个草图数据集编码为固定大小的潜在向量时，SketchINR对光栅和矢量草图的数据压缩率分别达到$60\times$和$10\times$。(ii) SketchINR的自解码器能提供比现有学习型矢量草图表示保真度更高的表征，且唯一具备扩展至FS-COCO等复杂矢量草图的处理能力。(iii) SketchINR支持并行化，其解码/渲染速度比SketchRNN等其他学习型矢量表示快约$\sim$$100\times$。(iv) SketchINR首次模拟了人类以不同笔划数量与复杂度绘制抽象草图的能力。作为对隐式草图的首次探索，SketchINR紧凑高保真的表示将为未来长序列复杂草图的建模研究提供有力支撑。