This is the torch implementation for paper "Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis"

This algorithm is for

• un-guided image synthesis (for example, classical texture synthesis)
• guided image synthesis (for example, transfer the style between different images)

• un-guided image synthesis

This code is based on Torch. It has only been tested on Mac and Ubuntu.

Dependencies:

• Torch
• loadcaffe
• CUDA
• cudnn

Pre-trained network: We use the the original VGG-19 model. You can find the download script at Neural Style. The downloaded model and prototxt file MUST be saved in the folder "data/models"

• Run qlua run_syn.lua in a terminal. The algorithm will create a synthesis image of twice the size as the style input image.
• The content/style images are located in the folders "data/content" and "data/style" respectively. Notice by default the content image is the same as the style image; and the content image is only used for initalization (optional).
• Results are located in the folder "data/result/freesyn/MRF"
• Parameters are defined & explained in "run_syn.lua".

• Run qlua run_trans.lua in a terminal. The algorithm will synthesis using the texture of the style image and the structure of the content image.
• The content/style images are located in the folders "data/content" and "data/style" respectively.
• Results are located in the folder "data/result/trans/MRF"
• Parameters are defined & explained in "run_trans.lua".

• Our algorithm requires efficient GPU memory to run. A Titan X (12G memory) is able to complete the above examples with default setting. For GPU with 4G memory or 2G memory, please use the reference parameter setting in the "run_trans.lua" and "run_syn.lua"

• This work is inspired and closely related to the paper: A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. The key difference between their method and our method is the different "style" constraints: While Gatys et al used a global constraint for non-photorealistic synthesis, we use a local constraint which works for both non-photorealistic and photorealistic synthesis. See our paper for more details.
• Our implementation is based on Justin Johnson's implementation of Neural Style.