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Implementation of NIPS 2017 paper Pose Guided Person Image Generation in PyTorch.


  • Python 3.6
  • PyTorch
  • Scikit-Image
  • Numpy
  • Scipy


The key task is to transfer the appearance of a person from a given pose to a desired pose keeping the important appearance details intact. A two-stage approach is proposed to address the task, with each stage focusing on one aspect. The first stage comprises of pose integration generating a coarse image. The second stage utilizes a variant of a conditional DCGAN to fill in more appearance details.

Network Architecture

The generation framework utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. The architecture of generator is inspired by U-Net in both the key stages. In the first stage the condition image and the target pose are fed into the network to generate a coarse image of the person with the target pose. The second stage then refines the blurry result by training a generator in an adversarial way. The architecture of the generator and discriminator are shown below:

Network Architecture


  • Clone the source code:

git clone
cd Pose-Guided-Image-Generation
  • Data preparation:

    1. Run ./ It creates a data folder in the root directory and downloads the data from the author's website. It extracts the zip file and processes the images for the train and test data sets.
    2. It creates the folders DF_train_data and DF_test_data in the directory data/DF_img_pose for the train and test data sets respectively printing the count of both the sets at the end.
  • Training Steps:

    1. Run ./ It performs the data preparation step if it has'nt been performed. The variable gpu in the script must be changed to -1 if the training process is carried on the cpu. Finally, training begins.
    2. It parses the namespaces present in the file and those passed explicitly. It also imports the models from the file and the PyTorch DataLoader from the file
    3. It trains the two generators and the discriminator for a specified number of epochs, printing the loss at each step. It also periodically saves the generator and discriminator models in the directory specified by argument checkpoint_dir in the file


The DeepFashion (In-shop Clothes Retrieval Benchmark) dataset consists of 52,712 in-shop clothes images, and 200,000 cross-pose/scale pairs. All images are in high-resolution of 256×256. The entire dataset can be downloaded from here.


Suggestions and pull requests are actively welcome.


  1. Pose Guided Person Image Generation. (Paper)
  2. A TensorFlow Implementation of Pose Guided Person Image Generation. (Link)