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Deep generative smoke simulator: connecting simulated and real data

  • Jinghuan Wen
  • Huimin MaEmail author
  • Xiong Luo
Original Article
  • 72 Downloads

Abstract

We propose a novel generative adversarial architecture to generate realistic smoke sequences. Physically based smoke simulation methods are difficult to match with real-captured data since smoke is vulnerable to disturbance. In our work, we design a generator that takes into account the temporal movement of smoke as well as detailed structures. With the help of convolutional neural networks and long short-term memory-based autoencoder, our generator can predict the future frames using temporal information while preserving details. We use generative adversarial networks to train the model on both simulated and real-captured data and propose a combined loss function that reflects both the physical laws and the data distributions. We also demonstrate a multi-phase training strategy that significantly speeds up convergence and increases stability of training on real-captured data. To test our approach, we set up experiments to capture real smoke sequences and show that our method can achieve realistic visual effects.

Keywords

Smoke simulation Generative adversarial networks Real data Autoencoder LSTM 

Notes

Funding

This work was funded by National Key Basic Research Program of China (No. 2016YFB0100900) and National Natural Science Foundation of China (No. 61773231).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

Supplementary material 1 (mp4 63522 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Tsinghua UniversityBeijingChina
  2. 2.University of Science and Technology BeijingBeijingChina

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