A repository of a paper named "Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?", accepted to ICRA 2023.

• Tested Environment
  • Ubuntu 20.04
  • Python 3.9 with Anaconda

1. Clone this repository, and move to root of this repo.
2. Prepare human3.6m dataset for euler angle based experiments. Save Folder h3.6m/ into ./data.
   • From issues, we just found out that the previous link for h3.6m has been shut down. One would be required to download the original dataset from human3.6m.
   • Inside the h3.6m folder, there should be sub-folders for each human subject, and that folder should contain .txt files (i.e., h3.6m/S1/directions_1.txt.)
3. Activate your conda environment, train pytorch based on your system, run pip install -r requirements.txt.
   • In my case, since I use cuda=11.6, I used command conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia to install pytorch.
4. Clone DLow repository.
5. Prepare dataset (human3.6m and humaneva) as DLow suggests, and put *.npz files to ./data and ./DLow/data.
6. Copy ./src/dlow/eval.py of our repository into DLow/motion_pred/eval.py.

• Before conducting experiments with Human 3.6M dataset, you need to run python preprocess.py first.
• After running the code, h36m_euler.pkl will be created in ./data.

python train.py --cfg h36m_euler_series_20step
python train.py --cfg h36m_euler_parallel_20step
python train.py --cfg h36m_xyz_series_20step
python train.py --cfg h36m_xyz_parallel_20step
python train.py --cfg humaneva_xyz_series_20step
python train.py --cfg humaneva_xyz_parallel_20step

• Your log will be stored to ./log/[TRAIN]*.txt.

• For deterministic prediction, run below code

  python eval.py --cfg h36m_euler_series_20step --mode stats
  python eval.py --cfg h36m_euler_parallel_20step --mode stats
  python eval.py --cfg h36m_xyz_series_20step --mode stats
  python eval.py --cfg h36m_xyz_parallel_20step --mode stats
  python eval.py --cfg humaneva_xyz_series_20step --mode stats
  python eval.py --cfg humaneva_xyz_parallel_20step --mode stats
  

• For plotting the prediction video, run below codes.

  python eval.py --cfg h36m_euler_series_20step --mode viz
  python eval.py --cfg h36m_euler_parallel_20step --mode viz
  python eval.py --cfg h36m_xyz_series_20step --mode viz
  python eval.py --cfg h36m_xyz_parallel_20step --mode viz
  python eval.py --cfg humaneva_xyz_series_20step --mode viz
  python eval.py --cfg humaneva_xyz_parallel_20step --mode viz
  

• For plotting the figure looks similar as we present in paper, please run below code.

  python first_figure.py --cfg h36m_euler_series_20step
  python first_figure.py --cfg h36m_euler_parallel_20step
  

• For visualizing how the noise becomes the data from the reverse process, run below code.

  python viz_diff_process.py --cfg h36m_euler_series_20step
  python viz_diff_process.py --cfg h36m_euler_parallel_20step
  

• Please find our pre-trained model on trained_models/dataName_poseType_modelType_stepSize/

• DLow codes are so well written! So (1) adding our config files and evalataion code to cloned DLow repository and (2) running the DLow code in DLow repository is enough. Below is the detailed process.

1. Copy ./src/dlow/eval.py of our repository into DLow/motion_pred/eval.py.

2. Copy your preprocessed data (i.e., data_3d_humaneva15.npz) into DLow/data.

3. Run DLow's training code as their README file instructs.

   python motion_pred/exp_vae.py --cfg h36m_nsamp50
   python motion_pred/exp_vae.py --cfg humaneva_nsamp50
   python motion_pred/exp_dlow.py --cfg h36m_nsamp50
   python motion_pred/exp_dlow.py --cfg humaneva_nsamp50
   

4. Run DLow's testing code as their README file instructs.

   python motion_pred/eval.py --cfg h36m_nsamp50 model stats
   python motion_pred/eval.py --cfg humaneva_nsamp50 model stats
   

5. To compare visualization between DLow and Diffusion model, run below code at the root of our repo.

   python viz_comp_dlow.py