CoSE: Compositional Stroke Embeddings

Understand

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.