SketchODE: Learning neural sketch representation in continuous timeDownload PDF

29 Sept 2021, 00:30 (edited 10 Mar 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: Chirography, Sketch, Free-form, Neural ODE
  • Abstract: Learning meaningful representations for chirographic drawing data such as sketches, handwriting, and flowcharts is a gateway for understanding and emulating human creative expression. Despite being inherently continuous-time data, existing works have treated these as discrete-time sequences, disregarding their true nature. In this work, we model such data as continuous-time functions and learn compact representations by virtue of Neural Ordinary Differential Equations. To this end, we introduce the first continuous-time Seq2Seq model and demonstrate some remarkable properties that set it apart from traditional discrete-time analogues. We also provide solutions for some practical challenges for such models, including introducing a family of parameterized ODE dynamics & continuous-time data augmentation particularly suitable for the task. Our models are validated on several datasets including VectorMNIST, DiDi and Quick, Draw!.
  • One-sentence Summary: Modelling continuous time chirographic structures like handwriting, diagrams, sketches etc with Neural Ordinary Differential Equations.
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