Official code release.
We use Anaconda to manage our python environment. Please install Anaconda and run the commands below to install dependencies and activate the environment:
conda env create -f environment.yml -n hallucinating-scenes
coonda activate hallucinating-scenes
We used pretrained Inception, VGG and OpenPose networks for evaluation and dataset construction. We also provide a pretrained checkpoint of our final model. Please download all of these checkpoints here. Place inception-2015-12-05.pt and vgg16.pt under metrics/pretrained/, open_pose.pt under open_pose/pretrained/, and ours.ckpt under checkpoints/ours/.
Please see the dataset page here for instructions to download the 10 source datasets and construct our meta-dataset. The dataset (or some subset of it) is necessary to train models as well as to provide input poses for generating images and evaluating the model.
See the file generate.ipynb for example code to generate images using our pretrained model. Note that both the pretrained model checkpoint and the dataset must be downloaded to generate images.
You can train a new model by running python run_train.py. We use Hydra to manage our experiment configurations. You can modify the config in configs/run_train/ours.yaml or by overriding parameters on the command line.
You can evaluate models by running python run_metrics.py MODEL_ID --dataset PATH_TO_DATASET.
To evaluate our pretrained model, set MODEL_ID to ours.
@inproceedings{brooks2021hallucinating,
title={Hallucinating Pose-Compatible Scenes},
author={Brooks, Tim and Efros, Alexei A},
booktitle=ECCV,
year={2022}
}