- Linux or macOS
- Python ≥ 3.6
- Install Docker and the NVIDIA Container Toolkit (see e.g. here)
- Build our docker image (from within the
garmentor/directory) with:or execute thedocker build -t <username>-garmentor docker
build.shscript from within thegarmentor/docker/directory:cd docker && bash build.sh - Update/Init the Git submodules
(tailornet_for_garmentor, tailornet_dataset_for_garmentor, lsun_for_garmentor). If it's the first time you check-out the submodules, use:
For any later submodule update, use:
git submodule update --init --recursivegit submodule update --recursive --remote - Setup data required by TailorNet, HierProb3D, and FrankMocap:
- TailorNet: Please refer to this document
- HierProb3D: Please refer to this document
- FrankMocap: Please refer to this document (install body model for Garmentor)
- Setup AGORA, 3DPW, SSP-3D datasets:
- AGORA: Please refer to this document (AGORA data (for Garmentor))
- 3DPW: Please refer to this document
- SSP-3D: Please refer to this document
- Make sure that the paths in
garmentor/data/const.pycorrespond to the locations in your installation - Adapt the
docker/run.shscript to mount the root directories that you previously created into the docker containerREPO_DIRshould point to the garmentor repositoryPW3D_DIRshould point to<3dpw_root>SSP3D_DIRshould point to<ssp-3d_root>TAILORNET_DATA_DIRshould point to<tailornet_data_root>HIERPROB3D_DATA_DIRshould point to<hierprob3d_data_root>
- To run the container with all data mounted in the correct places, you can now use
docker/run.sh
The following sections give an overview on how to use HierProb3D for inference and training. While they also contain instructions on how to setup the code and data, if you have followed the above six steps, you are already ready to go and can ignore these instructions.
You will need to download the SMPL model. The neutral model is required for training and running the demo code. If you want to evaluate the model on datasets with gendered SMPL labels (such as 3DPW and SSP-3D), the male and female models are available here. You will need to convert the SMPL model files to be compatible with python3 by removing any chumpy objects. To do so, please follow the instructions here.
Download pre-trained model checkpoints for our 3D Shape/Pose network, as well as for 2D Pose HRNet-W48 from here. In addition to the neutral-gender prediction network presented in the paper, we provide pre-trained checkpoints for male and female prediction networks, which are trained with male/female SMPL shape respectively. Download these checkpoints if you wish to do gendered shape inference.
Place the SMPL model files and network checkpoints in the model_files directory, which should have the following structure. If the files are placed elsewhere, you will need to update configs/paths.py accordingly.
HierarchicalProbabilistic3DHuman
├── model_files # Folder with model files
│ ├── smpl
│ │ ├── SMPL_NEUTRAL.pkl # Gender-neutral SMPL model
│ │ ├── SMPL_MALE.pkl # Male SMPL model
│ │ ├── SMPL_FEMALE.pkl # Female SMPL model
│ ├── poseMF_shapeGaussian_net_weights.tar # Pose/Shape distribution predictor checkpoint
│ ├── pose_hrnet_w48_384x288.pth # Pose2D HRNet checkpoint
│ ├── cocoplus_regressor.npy # Cocoplus joints regressor
│ ├── J_regressor_h36m.npy # Human3.6M joints regressor
│ ├── J_regressor_extra.npy # Extra joints regressor
│ └── UV_Processed.mat # DensePose UV coordinates for SMPL mesh
└── ...
run_predict.py is used to run inference on a given folder of input images. For example, to run inference on the demo folder, do:
python run_predict.py --image_dir ./demo/ --save_dir ./output/ --visualise_samples --visualise_uncropped
This will first detect human bounding boxes in the input images using Mask-RCNN. If your input images are already cropped and centred around the subject of interest, you may skip this step using --cropped_images as an option. The 3D Shape/Pose network is somewhat sensitive to cropping and centering - this is a good place to start troubleshooting in case of poor results.
If the gender of the subject is known, you may wish to carry out gendered inference using the provided male/female model weights. This can be done by modifying the above command as follows:
python run_predict.py --gender male --pose_shape_weights model_files/poseMF_shapeGaussian_net_weights_male.tar --image_dir ./demo/ --save_dir ./output_male/ --visualise_samples --visualise_uncropped
(similar for the female model). Using gendered models for inference may result in better body shape estimates, as it serves as a prior over 3D shape.
Inference can be slow due to the rejection sampling procedure used to estimate per-vertex 3D uncertainty. If you are not interested in per-vertex uncertainty, you may modify predict/predict_poseMF_shapeGaussian_net.py by commenting out code related to sampling, and use a plain texture to render meshes for visualisation (this will be cleaned up and added as an option to in the run_predict.py future).
run_evaluate.py is used to evaluate our method on the 3DPW and SSP-3D datasets. A description of the metrics used to measure performance is given in metrics/eval_metrics_tracker.py.
Download SSP-3D from here. Update configs/paths.py with the path pointing to the un-zipped SSP-3D directory. Evaluate on SSP-3D with:
python run_evaluate.py -D ssp3d
Download 3DPW from here. You will need to preprocess the dataset first, to extract centred+cropped images and SMPL labels (adapted from SPIN):
python -m data.pw3d_preprocess --dataset_path $3DPW_DIR_PATH
This should create a subdirectory with preprocessed files, such that the 3DPW directory has the following structure:
$3DPW_DIR_PATH
├── test
│ ├── 3dpw_test.npz
│ ├── cropped_frames
├── imageFiles
└── sequenceFiles
Additionally, download HRNet 2D joint detections on 3DPW from here, and place this in $3DPW_DIR_PATH/test. Update configs/paths.py with the path pointing to $3DPW_DIR_PATH/test. Evaluate on 3DPW with:
python run_evaluate.py -D 3dpw
The number of samples used to evaluate sample-related metrics can be changed using the --num_samples option (default is 10).
run_train.py is used to train our method using random synthetic training data (rendered on-the-fly during training).
Download .npz files containing SMPL training/validation body poses and textures from here. Place these files in a ./train_files directory, or update the appropriate variables in configs/paths.py with paths pointing to the these files. Note that the SMPL textures are from SURREAL and MultiGarmentNet.
We use images from LSUN as random backgrounds for our synthetic training data. Specifically, images from the 10 scene categories are used. Instructions to download and extract these images are provided here. The copy_lsun_images_to_train_files_dir.py script can be used to copy LSUN background images to the ./train_files directory, which should have the following structure:
train_files
├── lsun_backgrounds
├── train
├── val
├── smpl_train_poses.npz
├── smpl_train_textures.npz
├── smpl_val_poses.npz
└── smpl_val_textures.npz
Finally, start training with:
python run_train.py -E experiments/exp_001
As a sanity check, the script should find 91106 training poses, 125 + 792 training textures, 397582 training backgrounds, 33347 validation poses, 32 + 76 validation textures and 3000 validation backgrounds.
To run training with Visdom visualizations, specify the command line arguments (--vis or --vport <port>, or both), for example:
python run_train.py -E experiments/exp_001 --vis
# OR
python run_train.py -E experiments/exp_001 --port 8888
and track the progress in your browser on localhost:<port>, for example, localhost:888.
The current results are not yet competitive with the state-of-the-art. We are working on further improvements.
The code was adapted from/influenced by the following repos - thanks to the authors! Mostly, it was based on Akash Sengupta's repository: Hierarchical Probabilistic 3D Humans.