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DEEPWAVE: Deep Learning based Real-time Water Wave Simulation

Source code for course project in CS230 Virtual Reality.

Abstract

Water surface simulation is an important task in computer animation. Today’s simulation techniques are highly realistic. However, these techniques usually require extensive offline computation. It is difficult to compute the details of water wave in large phenomena. Effective real-time simulating techniques of water wave are necessary for many interactive applications such as virtual reality and games.

We present DEEPWAVE, a novel water wave simulation technique based on deep learning and wave packet theory. Instead of solving the physics equations, we use data-driven learning-based methods to do the simulation for wave packets. There are two models available in DEEPWAVE, namely packet-wise method and area-wise method. Packet-wise method runs inference for every packet while area-wise method runs inference for every sub area patch.

Experiment results show that both of them can effectively learn the motion of water wave. In addition, by training our models on better data, we can fix the artifacts produced by existing methods. Our models run very fast on modern GPU through highly optimized deep learning frameworks, so it also shows the potential to accelerate the existing CPU-based physical engine.

Example water wave simulated by DeepWave:

See My Paper and PDF of Presentation for more details

PDF of Presentation

Paper for this project

Requirements:

Visual Studio
Tensorflow == 1.3.0

Reference

Water Wave Packet
https://github.com/jeschke/water-wave-packets

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