Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketches as a sequence of low-dimensional parametric curves. To this end, we propose an inverse graphics framework capable of approximating a raster or waypoint based stroke encoded as a point-cloud with a variable-degree B\'ezier curve. Building on this module, we present Cloud2Curve, a generative model for scalable high-resolution vector sketches that can be trained end-to-end using point-cloud data alone. As a consequence, our model is also capable of deterministic vectorization which can map novel raster or waypoint based sketches to their corresponding high-resolution scalable B\'ezier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets.

本文提出一种基于逆向图形框架的可扩展高分辨率向量素描生成模型Cloud2Curve，该模型可以使用点云数据进行端到端训练，并具备确定性向量化功能，能够将新颖的栅格或航点素描映射到其相应的高分辨率比赛曲线，我们在Quick, Draw! 和K-MNIST数据集上评估了模型的生成和向量化功能。

Cloud2Curve：参数化草图的生成和矢量化