Great work! The fact that it captures "long-range order" seemingly perfectly is something not many have been able to do before! And the "collapse" visualization is great fun to watch.
But is your algorithm really qualitatively all that different from previous search methods (e.g. Efros and Leung), if you are still (uniform random?) sampling over the input distribution of patches?
I notice also your input textures tend to have sharp boundaries (as is common in pixel art). It would be interesting to see the results when the input has a lot of "noise", such as high def images of rocks or clouds ;)
While I still prefer search methods because they are easy to implement (and give better results), "deep" methods are definitely gaining some ground:
But is your algorithm really qualitatively all that different from previous search methods (e.g. Efros and Leung), if you are still (uniform random?) sampling over the input distribution of patches?
I notice also your input textures tend to have sharp boundaries (as is common in pixel art). It would be interesting to see the results when the input has a lot of "noise", such as high def images of rocks or clouds ;)
While I still prefer search methods because they are easy to implement (and give better results), "deep" methods are definitely gaining some ground:
Deep Textures
http://bethgelab.org/deeptextures/
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://arxiv.org/abs/1601.04589