Search Results for author:
Found 7 papers, 4 papers with code
TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates
We propose a novel approach to generate temporally coherent UV coordinates for loose clothing.
Reviving Autoencoder Pretraining
The pressing need for pretraining algorithms has been diminished by numerous advances in terms of regularization, architectures, and optimizers.
Data-driven Regularization via Racecar Training for Generalizing Neural Networks
We propose a novel training approach for improving the generalization in neural networks.
Learning General and Reusable Features via Racecar-Training
We propose a novel training approach for improving the learning of generalizing features in neural networks.
A Multi-Pass GAN for Fluid Flow Super-Resolution
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
Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.
Ranked #11 on
Video Super-Resolution
on Vid4 - 4x upscaling
(PSNR metric)
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows.
