no code implementations • 14 Jun 2022 • Mengyu Chu, Lingjie Liu, Quan Zheng, Erik Franz, Hans-Peter Seidel, Christian Theobalt, Rhaleb Zayer
With a hybrid architecture that separates static and dynamic contents, fluid interactions with static obstacles are reconstructed for the first time without additional geometry input or human labeling.
1 code implementation • 15 Jun 2019 • Sebastian Weiss, Mengyu Chu, Nils Thuerey, Rüdiger Westermann
With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multi-dimensional fields, for applications such as image super-resolution and scan completion.
1 code implementation • 4 Jun 2019 • Maximilian Werhahn, You Xie, Mengyu Chu, Nils Thuerey
We propose a novel method to up-sample volumetric functions with generative neural networks using several orthogonal passes.
13 code implementations • 23 Nov 2018 • Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixé, Nils Thuerey
Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.
Ranked #1 on Video Super-Resolution on MSU Video Upscalers: Quality Enhancement (VMAF metric)
2 code implementations • 29 Jan 2018 • You Xie, Erik Franz, Mengyu Chu, Nils Thuerey
We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows.
1 code implementation • 3 May 2017 • Mengyu Chu, Nils Thuerey
With the help of this patch advection, we generate stable space-time data sets from detailed fluids for our repositories.