Action2Motion: Conditioned Generation of 3D Human Motions

[Project Page] [Paper]

[2021/01/12] Updates: add evaluation related files & scripts

Action classifier models

• Motion feature extractors are pre-trained action classifiers, they are in ./model_file

Scripts

All other evaluation implementations are in ./eval_scripts

• Use load_classifier.py to load pre-trained models for calculating accuracy and FID. Argument input_size_raw is equal to joint_num*3.
• final_evaluation.py is the core file calculating the four metrics.
• Remember to import fid.py in final_evaluation.py.

Note the evaluation files are not directly runnable. But you should be able to reproduce our metrics with minor re-editting on them.

The codes of Dataloaders & Datasets could be found in this repo https://github.com/EricGuo5513/action2motion/tree/master/motion_loaders as reference.

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There are 4 steps to run this code

• Python Virtual Environment and dependencies
• Data download and preprocessing
• Training
• Test and Animation

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Python Virtual Environment

Anaconda is recommended to create the virtual environment

conda create -f environment.yaml
source activate torch-action2pose

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Data & Pre-trained Models

We use three datasets and they are: HumanAct12, NTU-RGBD and CMU Mocap. All datasets have been properly pre-transformed to better fit our purpose. Details are provided in our project webpage or dataset documents.

If you just want to play our pre-trained models without Lie version, you don't need to download datasets.

Create a folder for dataset

mkdir ./dataset/

Download HumanAct12 Dataset

If you'd like to use HumanAct12 dataset, download the data folder here, and place it in dataset/

Download NTU-RGBD Dataset

If you'd like to use NTU-RGBD dataset, download the data folder here, and place it in dataset/

Download CMU Mocap Dataset

If you'd like to use CMU-Mocap dataset, download the data folder here, and place it in dataset/

Our pre-trained models have been involved in folder checkpoints/. You don't need to download them additionally.

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Training

If you just want to play our pre-trained models, you could skip this step. We train the models using the script train_motion_vae.py. All the argments and their descriptions used for training are given in options/base_vae_option.py and options/train_vae_option.py. Some of them were used during trials, but may not be used in our paper. The argments used in examples are these which produce best performances during tuning.

• HumanAct12

python train_motion_vae.py --name <Experiment_name> --dataset_type humanact12 --batch_size 128 --motion_length 60 --coarse_grained --lambda_kld 0.001 --eval_every 2000 --plot_every 50 --print_every 20 --save_every 2000 --save_latest 50 --time_counter --use_lie --gpu_id 0 --iters 50000

All motions are of length 60.

• NTU-RGBD

python train_motion_vae.py --name <Experiment_name> --dataset_type ntu_rgbd_vibe  --batch_size 128 --motion_length 60 --lambda_kld 0.01 --eval_every 2000 --plot_every 50 --print_every 20 --save_every 2000 --save_latest 50 --time_counter --use_lie --gpu_id 0 --iters 50000 

All motions are of length 60.

• CMU Mocap

python train_motion_vae.py --name <Experiment_name> --dataset_type mocap  --batch_size 128 --motion_length 100 --lambda_kld 0.01 --eval_every 2000 --plot_every 50 --print_every 20 --save_every 2000 --save_latest 50 --time_counter --use_lie --gpu_id 0 --iters 50000 

All motions are of length 100.

Model files and intermediate data will be stored in ./checkpoints

Test and Animation

If you are generating results from models with Lie representation, you need to download the corresponding datasets and place them in/dataset. Because our model need to sample skeletons from real human datasets.

The animation results will appear in eval_results/

Play our model with Lie

• HumanAct12

python evaluate_motion_vae.py --name vanilla_vae_lie_mse_kld001 --dataset_type humanact12 --use_lie --time_counter --motion_length 60 --coarse_grained --gpu_id 0 --replic_times 5 --name_ext _R0

• NTU-RGBD

python evaluate_motion_vae.py --name vanilla_vae_lie_mse_kld01 --dataset_type ntu_rgbd_vibe --use_lie --time_counter --motion_length 60 --gpu_id 0 --replic_times 5 --name_ext R0 

• CMU Mocap

python evaluate_motion_vae.py --name vanilla_vae_lie_mse_kld01 --dataset_type mocap --use_lie --time_counter --motion_length 60 --gpu_id 0 --replic_times 5 --name_ext R0 

Play our model without Lie

• HumanAct12

python evaluate_motion_vae.py --name vanila_vae_tf --dataset_type humanact12  --motion_length 60 --coarse_grained --gpu_id 0 --replic_times 5 --name_ext R0

• NTU-RGBD

python evaluate_motion_vae.py --name vanila_vae_tf_2 --dataset_type ntu-rgbd-vibe  --motion_length 60 --gpu_id 0 --replic_times 2 --name_ext R0 

• CMU Mocap

python evaluate_motion_vae.py --name vanila_vae_tf_2 --dataset_type mocap  --motion_length 100 --gpu_id 0 --replic_times 2 --name_ext R0 

You could change the argument replic_times to get more generated motions. If you're testing the model you‘ve trained by you own, please replace the argument name with the name of checkpoint model you want to test.

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Citation

If you find this model or datasets useful for you research, please consider citing our work.

Misc

Contact Chuan Guo at cguo2 at ualberta.ca for any questions or comments