Exploring Neural Style Transfer with keras.
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Updated
Aug 29, 2018 - Python
Exploring Neural Style Transfer with keras.
This project try to better understand the power of the Gatys and al. texture synthesis algorithm by using news kind of cost function.
An Implementation of Texture Synthesis problem.
Controllable Multiple Textures Expansion & Transfer
Utilities for generating maximum entropy textures according to Victor and Conte 2012. **GitHub mirror**: development happens at https://gitlab.com/epiasini/metex
A Tensorflow implementation of multi-scale texture synthesis algorithm based on Gatys' deep texture algorithm.
Implementation and results from "Beyond GOTEX: Using Multiple Feature Detectors for Better Texture Synthesis"
Python script to generate tiled textures. Using PyGame for the display.
PyTorch code to reproduce the results of "Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints" by Liu et. al.
Erasing text using color segmentation, object tracking, and texture synthesis.
Tensorflow implementation of the Gatys‘ Texture Synthesis and Neural Style Transfer Algorithm
A package that allows to generate textures of arbitrary size with the ability to edit stylistic attributes in a meaningful way
PSGAN for Texture Synthesis, course project for "Deep Learning (DS-GA 1008)" at NYU
TextureBasedImageRetriever a Content Based Image Retriever that focuses on texture. It implements the offline phase which is the calulation of descriptors of all images in the datasetn, and the online phase that return the n-similar images from dataset given an input image.
Point-Pattern Synthesis using Gabor and Random Filters [EGSR 2022]
Thesis and supplementary material for "SVBRDF Texture Synthesis with Convolutional Autoencoders".
A python library for sampling image textures from the anisotropic fractional Brownian field.
Implementation of Texture Synthesis and Texture Transfer using Image Quilting. Based on the work of Alexei Efros and William Freeman. Course Project for CS 663.
Neural Texture Sound Synthesis exposing physically-driven continuous controls using synthetic-to-real unsupervised Domain Adaptation
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