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CoSE: Compositional Stroke Embeddings
Emre Aksan · Thomas Deselaers · Andrea Tagliasacchi · Otmar Hilliges

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #804

We present a generative model for stroke-based drawing tasks which is able to model complex free-form structures. 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 auto-encoder 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.

Author Information

Emre Aksan (ETH Zurich)
Thomas Deselaers (Apple)
Andrea Tagliasacchi (Google Research, Brain)
Otmar Hilliges (ETH Zurich)

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