Computer Animation and Virtual Worlds (Sepcial issue of CASA2019)
Sketch2VF: Sketch-Based Flow Design with Conditional Generative Adversarial Network
Zhongyuan Hu*, Haoran Xie**, Tsukasa Fukusato*,
Takahiro Sato* and Takeo Igarashi*
(University of Tokyo*, JAIST**)
Abstract
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We present an interactive user interface to support sketch-based fluid design with a perceptual understanding of human sketches.
In particular, the proposed system generates a 2D fluid animation from hand-drawn sketches. The proposed system utilizes a conditional
generative adversarial network model to generate stationary velocity fields from a sketch input. The network model is trained with
hand-drawn strokes and corresponding 2D velocity fields. On the basis of the generated velocity field, the system calculates fluid
dynamics using a semi-Lagrangian method. We ran a user study of the proposed system and confirmed
that the proposed interface is effective for a 2D fluid design and that the system achieves good results based on user input.
Sketch2VF GAN
Video
Publication
Zhongyuan Hu, Haoran Xie, Tsukasa Fukusato, Takahiro Sato and Takeo Igarashi. Sketch2VF: Sketch-Based Flow Design with Conditional Generative Adversarial Network. Computer Animation and Virtual Worlds (CAVW)，John Wiley, 2019. LINKAcknowledgements
This work was supported by JST CREST JPMJCR17A1, JSPS KAKENHI JP17H06574, JP19K20316, and HAYAO NAKAYAMA Foundation.We greatly thank Gabriel David Weymouth for sharing the fluid simulation codes.
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