AntiLibrary5 / taming-motion

Taming Motion

Trying an idea of learning a motion codebook from a dataset of human motion and learning the distribution of the codebook using a masked auto-encoding approach compared to an autoregressive transformer based approach.

Requirements

Create and activate a conda environment as follows:

conda env create -f environment.yaml
conda activate taming-motion
pip install git+https://github.com/nghorbani/body_visualizer.git
pip install git+https://github.com/MPI-IS/configer
pip install git+https://github.com/MPI-IS/mesh.git
pip install -e .

Note: ensure libboost-dev, gcc, g++ are installed on your system.

Using SMPL models

Since the SMPL fmaily of models require a licence agrrement, there is no automatic script to download them. Please follow the instructions in the smplx repository to do so.

NB: If you have access to a local inria machine (with acess to the LaaCie data depot) a ready to use folder can be found in /home/adakri/varora_lacies/LaCie/Models.

TO-DO

You will need to either download and process your own copy of HumanML3D or mount:

'/media/varora/LaCie/Datasets/HumanML3D/HumanML3D/'

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 --upgrade --force-reinstall

TO-DO

• Download HumanML3D dataset
• Visualize HumanML3D dataset
• Overfit a VQVAE codebook on a single sample
• Visualize reconstructions of overfitted VQVAE
• Train a VQVAE on whole HumanML3D dataset
• Visualize reconstructions of model trained on whole HumanML3D dataset
• Train a masked autoencoder on top of the learnt motion codebook

Results/Updates

https://gitlab.inria.fr/varora/taming-motion/-/issues/2

Notes

• Issue:

  ModuleNotFoundError: No module named 'mpl_toolkits'
  

  Solution:

  pip install basemap
  

• Issue

  ax.lines = []
  AttributeError: can't set attribute
  

  Solution:

  conda env create -f environment_t2m.yml 
  conda activate T2M-GPT
  

Dataset

Using the HumanML3D data: a 3D human motion-language dataset that originates from a combination of HumanAct12 and Amass dataset. Follow the instructions in the HumanML3D github repo to download and process the dataset.

Data directory: /media/varora/LaCie1/Datasets/HumanML3D/

Train VQVAE

python vqvae_motion.py --batch-size 256 --lr 2e-4 --total-iter 300000 --lr-scheduler 200000 --nb-code 512 --down-t 2 --depth 3 --dilation-growth-rate 3 --out-dir output --dataname t2m --vq-act relu --loss-vel 0.5 --recons-loss l1_smooth --exp-name motion-vqvae

Trained model: https://mybox.inria.fr/f/ef138f165e51480d8c53/?dl=1

Eval VQVAE

python vqvae_motion.py --batch-size 256 --lr 2e-4 --total-iter 300000 --lr-scheduler 200000 --nb-code 512 --down-t 2 --depth 3 --dilation-growth-rate 3 --out-dir output --dataname t2m --vq-act relu --quantizer ema_reset --loss-vel 0.5 --recons-loss l1_smooth --exp-name motion-vqvae-test --resume-pth output/motion-vqvae/net_last.pth --eval

Train GM3

python gm3.py --mask_ratio 0.6 --batch-size 256 --lr 2e-4 --total-iter 250000 --lr-scheduler 200000 --nb-code 512 --down-t 2 --depth 3 --dilation-growth-rate 3 --out-dir output --dataname t2m --vq-act relu --quantizer ema_reset --loss-vel 0.5 --recons-loss l1_smooth --exp-name gm3-train --resume-pth output/motion-vqvae/net_last.pth

Eval VQVAE with mask tokens

Download model from: https://mybox.inria.fr/f/9ce3b9c67ec344a2a97f/?dl=1 Place it as: output/motion-vqvae-with-mask-token/net_last.pth

python gm3.py --batch-size 256 --lr 2e-4 --total-iter 300000 --lr-scheduler 200000 --nb-code 512 --down-t 2 --depth 3 --dilation-growth-rate 3 --out-dir output --dataname t2m --vq-act relu --quantizer ema_reset --loss-vel 0.5 --recons-loss l1_smooth --exp-name motion-vqvae-with-mask-test-gifs --resume-pth output/motion-vqvae-with-mask-token/net_last.pth --with_mask_token

Visualize Results

tensorboard --logdir=<EXP-NAME>