jaedukseo/vunet

A generative model conditioned on shape and appearance.

A Variational U-Net for Conditional Appearance and Shape Generation

This repository contains training code for the CVPR 2018 spotlight

A Variational U-Net for Conditional Appearance and Shape Generation

The model learns to infer appearance from a single image and can synthesize images with that appearance in different poses.

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Requirements

The code was developed with Python 3. Dependencies can be installed with

pip install -r requirements.txt

Please note that the code does not work with tensorflow >= 1.3.0.

Training

Download and unpack the desired dataset. This results in a folder containing an index.p file. Either add a symbolic link named data pointing to the download directory or adjust the path to the index.p file in the <dataset>.yaml config file. To train the model, run

python main.py --config <dataset>.yaml

By default, images and checkpoints are saved to log/<current date>. To change the log directory and other options, see

python main.py -h

and the corresponding configuration file. To obtain images of optimal quality it is recommended to train for a second round with a loss based on Gram matrices. To do so run

python main.py --config <dataset>_retrain.yaml --retrain --checkpoint <path to checkpoint of first round>

Other Datasets

To be able to train the model on your own dataset you must provide a pickled dictionary with the following keys:

• joint_order: list indicating the order of joints.
• imgs: list of paths to images (relative to pickle file).
• train: list of booleans indicating if this image belongs to training split
• joints: list of [0,1] normalized xy joint coordinates of shape (len(joint_jorder), 2). Use negative values for occluded joints.

joint_order should contain

'rankle', 'rknee', 'rhip', 'rshoulder', 'relbow', 'rwrist', 'reye', 'lankle', 'lknee', 'lhip', 'lshoulder', 'lelbow', 'lwrist', 'leye', 'cnose'

and images without valid values for rhip, rshoulder, lhip, lshoulder are ignored.