Official Implementation of the paper "Implicit Neural Representations for Variable Length Human Motion Generation" (ECCV 2022)

[More Visualizations]

Bibtex

Please consider citing this work, if you find this code useful.

@article{cervantes2022implicit,
  title={Implicit Neural Representations for Variable Length Human Motion Generation},
  author={Cervantes, Pablo and Sekikawa, Yusuke and Sato, Ikuro and Shinoda, Koichi},
  journal={arXiv preprint arXiv:2203.13694},
  year={2022}
}

---

Installation

pip install -r requirements.txt
pip install torch==1.8.1+cu101 torchvision==0.9.1+cu101 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

To install Pytorch3D follow the instructions here.

For CUDA builds with versions earlier than CUDA 11, set CUB_HOME

pip install "git+https://github.com/facebookresearch/pytorch3d.git@v0.3.0"

---

Data Preparation

Download datasets

Download evaluation models

Download SMPL model

---

Usage

Training

python3 ./ImplicitMotion/main.py 
	--path_config=/path/to/config-file

Before training you need to prepare a configuration file. Configurations for the experiments in the paper are provided here. Modifications for the following keyword arguments are necessary:

path_dataset: Path to dataset folder
path_results_base: Path to folder for saving checkpoints, etc. (arbitrary).
path_smpl: Path to SMPL file (.pkl)

Evaluation

python3 ./ImplicitMotion/test/test_metric.py 
	--path_results /path/to/results
	--path_classifier /path/to/classifier
	--variable_length_testing
	--metrics

Visualization

python3 ./ImplicitMotion/test/test_metric.py
	--path_results /path/to/results
	--path_classifier /path/to/classifier
	--variable_length_testing
	--video
	--num_videos 1
	--video_length 60

---

License

This code is distributed under an MIT LICENSE.