Search Results for author: Mengyu Chu

Found 6 papers, 5 papers with code

Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data

no code implementations14 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.

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

1 code implementation15 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.

Image Super-Resolution

A Multi-Pass GAN for Fluid Flow Super-Resolution

1 code implementation4 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.


Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

13 code implementations23 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.

Image Super-Resolution Motion Compensation +3

tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow

1 code implementation29 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.

Data Augmentation Super-Resolution

Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors

1 code implementation3 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.

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