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.

中文翻译：

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深度生成烟雾模拟器：连接模拟数据和真实数据

我们提出了一种新颖的生成对抗架构来生成逼真的烟雾序列。由于烟雾容易受到干扰，基于物理的烟雾模拟方法很难与实际捕获的数据相匹配。在我们的工作中，我们设计了一个发生器，它考虑了烟雾的时间运动以及详细的结构。在卷积神经网络和基于长短期记忆的自动编码器的帮助下，我们的生成器可以使用时间信息预测未来的帧，同时保留细节。我们使用生成对抗网络在模拟和真实捕获的数据上训练模型，并提出了一个反映物理定律和数据分布的组合损失函数。我们还展示了一种多阶段训练策略，可显着加快收敛速度​​并提高对真实捕获数据的训练稳定性。为了测试我们的方法，我们设置了实验来捕捉真实的烟雾序列，并表明我们的方法可以实现逼真的视觉效果。