Lifting Monocular Events to 3D Human Poses

• Train classification models based on ResNet18, Resnet34, ...
• Train 3D reconstruction models
• Dataset adpatation for DHP19 dataset
• Generate events from events dataset with different frames representations (constant-count, spatiotemporal voxelgrid)

Table of contents

• Environment
• Data
• Model zoo
• Agents

Environment

Create a virtualenv environment from requirements.txt. Using pipenv:

pipenv install -r requirements.txt
pipenv shell
python -m pip install .

Data

DHP19

Follow DHP19 guide at scripts/dhp19/README.md

Events-H3m

Follow the guide at scripts/h3m/README.md

Model zoo

A model zoo of backbones and models for constant_count and voxelgrid trained both with DHP19 and Events-H3m is publicly accessible at this link

Agents

Train and evaluate for different tasks

If you want to launch an experiment with default parameters (backbone ResNet50, DHP19 with constant-count representation, see the paper for details), you simply do (after setup and data):

python train.py 

A complete configuration is provided at ./confs/train/config.yaml. In particular, refer to ./confs/train/dataset/... for dataset configuration (including path specification), and to ./confs/train/training for different tasks.

If you want to continue an ended experiment, you can set training.load_training to true and provide a checkpoint path:

python train.py training.load_training=true training.load_path={YOUR_MODEL_CHECKPONT}

To continue a previous experiment:

python train.py training.load_training=true training.load_path={YOUR_MODEL_CHECKPONT}

To train a margipose_estimator agent:

python scripts/train.py training=margipose dataset=$DATASET training.model=$MODEL training.batch_size=$BATCH_SIZE training.stages=$N_STAGES

Supported dataset are: constantcount_h3m, voxelgrid_h3m, constantcount_dhp19, voxelgrid_dhp19 To evaluate a model, you can use:

python scripts/eveluate.py training.load_path={YOUR_MODEL_CHECKPOINT}

Test

You can test your models using our multi-movement evaluation script. The tool generates a result.json file in the provided checkpoint path.

python evaluate_dhp19.py training={TASK} dataset={DATASET_REPRESENTATION} load_path={YOUR_MODEL_CHECKPOINT}

This framework is intended to be fully extensible. It's based upon pytorch_lighting [1] and hydra configuration files.

References

[1] Falcon, WA and .al (2019). PyTorch Lightning GitHub. Note: https://github.com/PyTorchLightning/pytorch-lightning