Deep Learning for Graphics Course at SIGGRAPH Asia 2018

We yesterday held our course on Deep Learning for Graphics Course at SIGGRAPH Asia 2018 in Tokyo. The slides are now available online at:

http://geometry.cs.ucl.ac.uk/creativeai/

Abstract: In computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In an increasing variety of problem settings, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.

 

Video Super-resolution with Deep Learning

Our work on video super-resolution with GANs is online now as a preview. The main trick is a special discriminator CNN that learns to supervise in terms of detail as well as temporal coherence. In addition, we propose a novel set of metrics for quantifying temporal coherence in videos. Enjoy 🙂 !

Abstract: Adversarial training has been highly successful in the context of image super-resolution. It was demonstrated to yield realistic and highly detailed results. Despite this success, many state-of-the-art methods for video super-resolution still favor simpler norms such as L_2 over adversarial loss functions. This is caused by the fact that the averaging nature of direct vector norms as loss functions leads to temporal smoothness. The lack of spatial detail means temporal coherence is easily established. In our work, we instead propose an adversarial training for video super-resolution that leads to temporally coherent solutions without sacrificing spatial detail.

In our generator, we use a recurrent, residual framework that naturally encourages temporal consistency. For adversarial training, we propose a novel spatio-temporal discriminator in combination with motion compensation to guarantee photo-realistic and temporally coherent details in the results. We additionally identify a class of temporal artifacts in these recurrent networks, and propose a novel Ping-Pong loss to remove them. Quantifying the temporal coherence for image super-resolution tasks has also not been addressed previously. We propose a first set of metrics to evaluate the accuracy as well as the perceptual quality of the temporal evolution, and we demonstrate that our method outperforms previous work by yielding realistic and detailed images with natural temporal changes.

Physics-based Deep Learning at NIPS 2018

We will be presenting our recent works on physics-based deep learning for fluid flow at the NIPS 2018 workshop on “Modeling the Physical World: Learning, Perception, and Control“, organized by Jiajun Wu, Kelsey Allen, Kevin Smith, Jessica Hamrick, Emmanuel Dupoux, Marc Toussaint, and Joshua Tenenbaum.

NIPS Conference: https://nips.cc

NIPS 2018 Workshop “Modeling the Physical World: Learning, Perception, and Control”: https://nips.cc/Conferences/2018/Schedule?showEvent=10931

Workshop homepage: http://phys2018.csail.mit.edu/submission.html

In particular we will discuss our works on:

Detailed abstracts:

Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow: Our work explores methods for the data-driven inference of temporal evolutions of physical functions with deep learning techniques. More specifically, we target fluid flow problems, and we propose a novel network architecture to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. Key for arriving at a feasible algorithm is a technique for dimensionality reduction based on convolutional neural networks, as well as a special architecture for temporal prediction. We demonstrate that dense 3D+time functions of physics system can be predicted with neural networks, and we arrive at a neural-network based simulation algorithm with practical speed-ups. We demonstrate the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase simulations. Our method predicts pressure fields very efficiently. It is more than two orders of magnitudes faster than a regular solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.

Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow: We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. Our work represents the 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.

Coupled Fluid Density and Motion from Single Views: We present a novel method to reconstruct a fluid’s 3D density and motion based on just a single sequence of images. This is rendered possible by using powerful physical priors for this strongly under-determined problem. More specifically, we propose a novel strategy to infer density updates strongly coupled to previous and current estimates of the flow motion. Additionally, we employ an accurate discretization and depth-based regularizers to compute stable solutions. Using only one view for the reconstruction reduces the complexity of the capturing setup drastically and could even allow for online video databases or smart-phone videos as inputs. The reconstructed 3D velocity can then be flexibly utilized, e.g., for re-simulation, domain modification or guiding purposes. We will demonstrate the capacity of our method with a series of synthetic test cases and the reconstruction of real smoke plumes captured with a Raspberry Pi camera.

 

Reynolds-averaged Navier Stokes Simulations with Deep Learning

Our paper on Reynolds-averaged Navier Stokes simulations with deep learning (i.e. convolutional neural networks) is online now. You can read it on arXiv:
https://arxiv.org/abs/1810.08217

The full source code and a first training data set with 6400 RANS simulations as ground truth data is available here:
https://github.com/thunil/Deep-Flow-Prediction

Deep Flow Prediction is a pytorch framework for fluid flow (Reynolds-averaged Navier Stokes) predictions with deep learning. It contains code for data generation, network training, and evaluation for the aforementioned paper. A brief manual for the ode can be found in the github repository.

Paper Details:
Title: Well, how accurate is it? A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations

Authors: Nils Thuerey, Konstantin Weissenow, Harshit Mehrotra, Nischal Mainali, Lukas Prantl, Xiangyu Hu

Abstract: With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.

Details

Real-time Fluid Simulations at the TUM Open House 2018

We’are currently (as of Oct. 13) demoing our deformation neural network app and real-time fluid solver at the TUM open house 2018.

If you have time today – stop by at Garching Forschungszentrum 🙂

Otherwise you can check out the corresponding papers:

You can of course also check out our “Neural Liquid Drop” App on the Android app store.

SIGGRAPH 2018 tempoGAN talk on YouTube

You Xie and Rachel Chu just successfully presented their paper “tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow” at SIGRGAPH 2018. You can enjoy their full presentation via the YouTube video below.

Presentation:

More info regarding the SIGGRAPH 2018 technical papers:

Technical Papers

 

 

tempoGAN paper abstract:

We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. Our work represents the 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.

 

tempoGAN source code now online

The source code for our tempoGAN project, which aims for super-resolution inference of Navier-Stokes transport phenomena (such as smoke clouds) is online now at: https://github.com/thunil/tempoGAN

It comes with a readme, data generation scripts, and should give an easy starting point for training generative adversarial nets for fluids. If you try it, let us know how it works!

Project page

Here’s again the full abstract of the paper: We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. Our work represents the 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.

Best-paper award for “Coupled Fluid Density and Motion from Single Views” at SCA 2018

Congratulations to Marie-Lena Eckert for winning the best-paper award at SCA 2018 for her submission “Coupled Fluid Density and Motion from Single Views”.

Her work aims for reconstructing fluid flow phenomena with strong Navier-Stokes priors. This makes it possible to compute dense flow fields based on only a monocular video, i.e., an image sequence from a single viewpoint.

Paper Abstract: We present a novel method to reconstruct a fluid’s 3D density and motion based on just a single sequence of images. This is rendered possible by using powerful physical priors for this strongly under-determined problem. More specifically, we propose a novel strategy to infer density updates strongly coupled to previous and current estimates of the flow motion. Additionally, we employ an accurate discretization and depth-based regularizers to compute stable solutions. Using only one view for the reconstruction reduces the complexity of the capturing setup drastically and could even allow for online video databases or smart-phone videos as inputs. The reconstructed 3D velocity can then be flexibly utilized, e.g., for re-simulation, domain modification or guiding purposes.

More information about the ACM SIGGRAPH / Eurographics Symposium on Computer Animation: (SCA): http://sca2018.inria.fr

Coupled Fluid Density and Motion from Single Views