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Found 7 papers, 3 papers with code
Real-Time Neural Materials using Block-Compressed Features
We present a neural material model whose features and decoder are specifically designed to be used in real-time rendering pipelines.
Inverse problem regularization with hierarchical variational autoencoders
Our experiments show that the proposed PnP-HVAE method is competitive with both SOTA denoiser-based PnP approaches, and other SOTA restoration methods based on generative models.
Diverse super-resolution with pretrained deep hiererarchical VAEs
We investigate the problem of producing diverse solutions to an image super-resolution problem.
Wasserstein Patch Prior for Image Superresolution
Then, the proposed regularizer penalizes the $W_2$-distance of the patch distribution of the reconstruction to the patch distribution of some reference image at different scales.
Learning local regularization for variational image restoration
In this work, we propose a framework to learn a local regularization model for solving general image restoration problems.
On the Existence of Optimal Transport Gradient for Learning Generative Models
Training of WGAN relies on a theoretical background: the calculation of the gradient of the optimal transport cost with respect to the generative model parameters.
A Generative Model for Texture Synthesis based on Optimal Transport between Feature Distributions
The GOTEX model based on patch features is also adapted to texture inpainting and texture interpolation.
