Our work explores temporal self-supervision for GAN-based video generation tasks. While
adversarial training successfully yields generative models for a variety of areas, temporal …

Physics-based fluid simulation has played an increasingly important role in the computer
graphics community. Recent methods in this area have greatly improved the generation of …

This paper proposes a new data‐driven approach to model detailed splashes for liquid
simulations with neural networks. Our model learns to generate small‐scale splash detail for …

Adversarial training has been highly successful for single-image super-resolution, as it
yields realistic and highly detailed results. Despite this success, current stateof-the-art …

In this paper, we present ScalarFlow, a first large-scale data set of reconstructions of real-
world smoke plumes. In addition, we propose a framework for accurate physics-based …

We propose a method for simulating the complex dynamics of partially and fully saturated
woven and knit fabrics interacting with liquid, including the effects of buoyancy, nonlinear …

We introduce FontCode, an information embedding technique for text documents. Provided
a text document with specific fonts, our method embeds user-specified information in the text …

Standard liquid simulators apply operator splitting to independently solve for pressure and
viscous stresses, a decoupling that induces incorrect free surface boundary conditions. Such …

This article presents an interactive method for 3D cloud animation at the landscape scale by
employing machine learning. To this end, we utilize deep convolutional generative …

Over the last two decades there has been a proliferation of methods for simulating crowds of
humans. As the number of different methods and their complexity increases, it becomes …