tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
This work addresses the challenge of generating detailed and consistent fluid simulations for applications in computer graphics and physics, representing a first approach in this domain.
The authors tackled the problem of super-resolution for fluid flows by introducing tempoGAN, a generative model that synthesizes four-dimensional physics fields with neural networks, achieving realistic and temporally coherent high-resolution details using low-resolution inputs like velocities or vorticities.
We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. Our work represents a first approach to synthesize four-dimensional physics fields with neural networks. Based on a conditional generative adversarial network that is designed for the inference of three-dimensional volumetric data, our model generates consistent and detailed results by using a novel temporal discriminator, in addition to the commonly used spatial one. Our experiments show that the generator is able to infer more realistic high-resolution details by using additional physical quantities, such as low-resolution velocities or vorticities. Besides improvements in the training process and in the generated outputs, these inputs offer means for artistic control as well. We additionally employ a physics-aware data augmentation step, which is crucial to avoid overfitting and to reduce memory requirements. In this way, our network learns to generate advected quantities with highly detailed, realistic, and temporally coherent features. Our method works instantaneously, using only a single time-step of low-resolution fluid data. We demonstrate the abilities of our method using a variety of complex inputs and applications in two and three dimensions.