Artistic Style TransferNicholas Tan, Elias Wang
Department of Electrical Engineering, Stanford University
Motivation Algorithm Overview
Related Work
Experimental ResultsUsing a biologically inspiredvision model, called “DeepNeural Networks,” anartificial system can betrained to learn differentstyles of painting andsubsequently recreateimages rendered in thatstyle.
The model is successfulbecause it is able todistinguish between “style”vs. “content”, and mergethe two.
Using a modification ofTexture-Synthesis it ispossible to get fast andcomparable style-transferresults. This method focuseson separating information-poor areas to hallucinate thestyle and high content areaswhere it keeps the originalimage
Content Image Style Image
Color transfer Add noise
Resize Estimate(finer scale)
Input:
SegmentationInitialization:
Loop over scales:Loop over patch sizes:
Patch Matching Style Synthesis
Content Fusion
Color Transfer
Denoise
Resize Patch
Hallucination
Chosen parameters: 3 scales, 2 patch sizes (except smallest scale uses 3 patch sizes), 10% NN noise, 25%+75% hallucination averaging
0% 10% 25% 50%
Hallucination percentage influence :
Content
Style
Source: L.A. Gatys, A.S. Ecker, and M. Bethge, Image Style Transfer Using Convolutional Neural Networks, CVPR, 2016.
Source: M. Elad, P. Milanfar, Style-Transfer via Texture-Synthesis, Google Research, September 21, 2016
Source: V. Kwatra, I. Essa, A. Bobick, and N. Kwatra, Texture Optimization for Example-Based Synthesis,ACM ToG, Vol. 24, No. 3, pp. 795-802, 2005
Style Fusion