SketchODE: Learning neural sketch representation in continuous time

Ayan Das, Yongxin Yang, Timothy M Hospedales, Tao Xiang, Yi-Zhe Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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!.
Original languageEnglish
Title of host publicationInternational Conference on Learning Representations (ICLR 2022)
Number of pages16
Publication statusPublished - 25 Apr 2022
EventTenth International Conference on Learning Representations 2022 - Virtual Conference
Duration: 25 Apr 202229 Apr 2022
Conference number: 10


ConferenceTenth International Conference on Learning Representations 2022
Abbreviated titleICLR 2022
Internet address


  • Chirography
  • Sketch
  • Free-form
  • Neural ODE


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