论文标题
Cose：组成式嵌入
CoSE: Compositional Stroke Embeddings
论文作者
论文摘要
我们为复杂的自由形式结构（例如基于中风的绘图任务）提供了生成模型。虽然先前的方法依靠基于序列的模型来用于基本对象或手写文本的图纸，但我们提出了一个模型，将图形视为可以组成的笔画集合，这些模型可以组成，这些模型可以组成复杂的结构，例如图表（例如，流程图）。该方法的核心是一种新颖的自动编码器，该自动编码器将可变长度的笔触投射到固定尺寸的潜在空间中。该表示空间允许在潜在空间中运行的关系模型更好地捕获笔触之间的关系并预测随后的笔触。我们在定性和定量上证明我们所提出的方法能够建模单个笔触的外观以及大图图的组成结构。我们的方法适用于交互式用例，例如自动完成图。我们在https://eth-ait.github.io/cose上公开提供代码和模型。
We present a generative model for complex free-form structures such as stroke-based drawing tasks. While previous approaches rely on sequence-based models for drawings of basic objects or handwritten text, we propose a model that treats drawings as a collection of strokes that can be composed into complex structures such as diagrams (e.g., flow-charts). At the core of the approach lies a novel autoencoder that projects variable-length strokes into a latent space of fixed dimension. This representation space allows a relational model, operating in latent space, to better capture the relationship between strokes and to predict subsequent strokes. We demonstrate qualitatively and quantitatively that our proposed approach is able to model the appearance of individual strokes, as well as the compositional structure of larger diagram drawings. Our approach is suitable for interactive use cases such as auto-completing diagrams. We make code and models publicly available at https://eth-ait.github.io/cose.
