This is the reimplementation code of CVPR'2018 paper Learning to Sketch with Shortcut Cycle Consistency.

Photo	Generated examples

• Python 3

• Tensorflow (>= 1.4.0)

• InkScape or CairoSVG (For vector sketch rendering. Choose one of them is ok.)

  sudo apt-get install inkscape
  # or
  pip3 install cairosvg
  

From the paper, we need to pre-train the model on the QuickDraw dataset. So we need to preprocess both the QuickDraw-shoes and QMUL-shoes data following these steps:

1. QuickDraw-shoes

   • Download the sketchrnn_shoes.npz data from QuickDraw
   • Place the package under datasets/QuickDraw/shoes/npz/ directory
   • run the command:

     python quickdraw_data_processing.py
     

2. QMUL-shoes

   • Download the photo data from QMUL-Shoe-Chair-V2
   • Unzip the ShoeV2_photo package and place all .png under datasets/QMUL/shoes/photos/ directory
   • Download the preprocessed sketch data from here and place the two .h5 packages under datasets/QMUL/shoes/ directory

1. QuickDraw-shoes pre-training

   • Change the value to QuickDraw in model.py-get_default_hparams-data_type
   • run the command:

     python sketch_p2s_train.py
     

2. QMUL-shoes training

   • Change the value to QMUL in model.py-get_default_hparams-data_type
   • Make sure the QuickDraw-shoes pre-training models/checkpoint are placed under outputs/snapshot/ directory
   • Change the value to True in sketch_p2s_train.py-resume_training
   • run the command:

     python sketch_p2s_train.py
     

The following figure shows the total loss, KL loss and reconstruction loss during training with QuickDraw-shoes pre-trained within 30k iterations and the following QMUL-shoes trained within 40k iterations.

1. QuickDraw-shoes

   • Make sure the value of data_type to be QuickDraw in model.py
   • Place models/checkpoint/config under outputs/snapshot/QuickDraw/ directory
   • run the command:

     python sketch_p2s_sampling.py
     

2. QMUL-shoes

   • Make sure the value of data_type to be QMUL in model.py
   • Place models/checkpoint/config under outputs/snapshot/QMUL/ directory
   • run the command:

     python sketch_p2s_sampling.py
     

All results can be found under outputs/sampling/ dir.

• This code is largely borrowed from repos Sketch-RNN and deep_p2s.