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AGRoL: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model

Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model Y. Du, R. Kips, A. Pumarola, S. Starke, A. Thabet, A. Sanakoyeu CVPR 2023

[arXiv] [Project] [BibTeX]

Enviroment Setup

All our experiments are done on a single V-100 16G GPU.

conda env create -f environment.yml
conda activate agrol

The code was tested on Python 3.9 and PyTorch 1.12.1.

Download the human_body_prior lib and body_visualizer lib and put them in this repo. The repo should look like

agrol
├── body_visualizer
├──── mesh/
├──── tools/
├──── ...
├── human_body_prior/
├──── body_model/
├──── data/
├──── ...
├── dataset/
├── prepare_data/
└── ...

Dataset Preparation

Please download the AMASS dataset from here(SMPL+H G).

python prepare_data.py --support_dir /path/to/your/smplh/dmpls --save_dir ./dataset/AMASS/ --root_dir /path/to/your/amass/dataset

The generated dataset should look like this

./dataset/AMASS/
├── BioMotionLab_NTroje
├──── train/
├──── test/
├── CMU/
├──── train/
├──── test/
└── MPI_HDM05/
├──── train/
└──── test/

Evaluation

You can either download our pre-trained models or use your pre-trained model. To download our pre-trained models:

sh prepare_data/download_model.sh

To evaluate the model:

# Diffusion model
python test.py --model_path /path/to/your/model --timestep_respacing ddim5 --support_dir /path/to/your/smpls/dmpls --dataset_path ./dataset/AMASS/

# MLP
python test.py --model_path /path/to/your/model --support_dir /path/to/your/smpls/dmpls --dataset_path ./dataset/AMASS/

Training

To train the AGRoL diffusion-model:

python train.py --save_dir /path/to/save/your/model --dataset amass --weight_decay 1e-4 --batch_size 256 --lr 3e-4 --latent_dim 512 --save_interval 1 --log_interval 1 --device 0 --input_motion_length 196 --diffusion_steps 1000 --num_workers 8 --motion_nfeat 132 --arch diffusion_DiffMLP --layers 12 --sparse_dim 54 --train_dataset_repeat_times 1000 --lr_anneal_steps 225000 --overwrite

To train the MLP model:

python train.py --save_dir /path/to/save/your/model --dataset amass --weight_decay 1e-4 --batch_size 256 --lr 3e-4 --latent_dim 512 --save_interval 1 --log_interval 1 --device 0 --input_motion_length 196 --diffusion_steps 1000 --num_workers 8 --motion_nfeat 132 --arch mlp_PureMLP --layers 12 --sparse_dim 54 --train_dataset_repeat_times 1000 --lr_anneal_steps 225000 --overwrite --no_normalization

Pretrained Weights

The pretrained weights for AGRoL can be downloaded from this link: https://github.com/facebookresearch/AGRoL/releases/tag/v0

To download the wights automatically, please run bash prepare_data/download_model.sh.

To test the pretrained AGRoL diffusion-model:

python test.py --model_path pretrained_weights/diffmlp.pt --timestep_respacing ddim5 --support_dir /path/to/your/smpls/dmpls --dataset_path ./dataset/AMASS/

To visualize the generated motions, add these commands behind:

--vis --output_dir /path/to/save/your/videos

License

CC BY-NC 4.0

The majority of AGRoL code is licensed under CC-BY-NC, however portions of the project are available under separate license terms:

  • Trimesh, AvatarPose, Guided Diffusion, and MDM are licensed under the MIT license;
  • Human Body Prior is licensed under a custom license for non-commercial scientific research purposes, available at link;
  • Body Visualizer is licensed under a custom license for non-commercial scientific research purposes, available at link.

Citing AGRoL

If you find our work inspiring or use our codebase in your research, please consider giving a star ⭐ and a citation.

@inproceedings{du2023agrol,
  author    = {Du, Yuming and Kips, Robin and Pumarola, Albert and Starke, Sebastian and Thabet, Ali and Sanakoyeu, Artsiom},
  title     = {Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model},
  booktitle = {CVPR},
  year      = {2023},
}

Trouble Shooting

If you encounter this error during visualization:

ValueError: Cannot use face colors with a smooth mesh

You can fix it by changing the line 88 in your body_visualizer/mesh/mesh_viewer.py to:

mesh = pyrender.Mesh.from_trimesh(mesh, smooth=False)