ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation (ICCV 2023)

Project Page | Paper

Overview

We propose a GAN-based Transformer for general action-conditioned 3D human motion generation, including single-person actions and multi-person interactive actions.

Environment

It is highly encouraged to set up an independent environment with Conda.

conda create --name actformer python=3.8
conda activate actformer

This code implementation is based on PyTorch. We have tested with pytorch 1.5.0, while other close or newer versions should also work. Please first install a PyTorch version which fits your compute platform.

Then run the following command to install other dependencies.

pip install -r requirements.txt

Data Preparation

NTU RGB+D 120

• Download skeleton data of the NTU RGB+D 120 dataset and the record about samples with missing skeletons.
• Put the downloaded data in a same path: <path_to_downloaded_data>.
• Run our preprocessing code.

1. python datasets/ntu120/prepare_ntu_data.py -s <path_to_downloaded_data>
2. python datasets/ntu120/split_train_test.py -d datasets/ntu120/data1p/ntu120.h5 -o datasets/ntu120/data1p
3. python datasets/ntu120/split_train_test.py -d datasets/ntu120/data2p/ntu120.h5 -o datasets/ntu120/data2p --two_person

NTU RGB+D 120 SMPL

• Download the RGB videos of NTU RGB+D 120 dataset and put them in the following directory: <ntu_video_dir>.

<ntu_video_dir>
└───nturgb+d_rgb_s001
└───nturgb+d_rgb_s002
└───...

• Extract the SMPL parameters from videos using aistplusplus_api.
• Run our preprocessing code datasets/ntu120_smpl/prepare_data.py. Please modify the SRC to the folder of the extracted SMPL results.

1. python datasets/ntu120_smpl/prepare_data.py
2. python split_1p.py

BABEL

• Download MoCap data from AMASS and semantic labels from BABEL.
• Put the downloaded data in a same path.
• Run our preprocessing code.

python datasets/babel/prepare_data.py -s <path_to_downloaded_data>

GTA Combat Dataset

For the GTA Combat Dataset, you can directly download the processed files from Google Drive or 百度云下载链接.

SMPL files

Download the SMPL neutral models from the SMPL website and move them to data/smpl.

smpl/
|-- J_regressor_extra.npy
|-- SMPL_NEUTRAL.pkl
|-- kintree_table.pkl
`-- smplfaces.npy

Train

• ActFormer

# NTU RGB+D 120 (1P)
python main.py generator -c config/generator/ntu120_1p/actformer.yaml

# BABEL
python main.py generator -c config/generator/babel/actformer.yaml

# NTU RGB+D 120 (2P)
python main.py generator -c config/generator/ntu120_2p/actformer.yaml

# GTA Combat (2P)
python main.py generator -c config/generator/gta/actformer_2p.yaml  # Similar for 3P / 4P/ 5P

Generate with trained model

# NTU RGB+D 120 (1P)
python main.py generator -c config/generator/ntu120_1p/actformer.yaml --phase gen --model_weights <path_to_trained_model (.pt file)> --work_dir work_dir/generate/ntu120_1p

# BABEL
python main.py generator -c config/generator/babel/actformer.yaml --phase gen --model_weights <path_to_trained_model (.pt file)> --work_dir work_dir/generate/babel

# NTU RGB+D 120 (2P)
python main.py generator -c config/generator/ntu120_2p/actformer.yaml --phase gen --model_weights <path_to_trained_model (.pt file)> --work_dir work_dir/generate/ntu120_2p

Visualization

Code for visualizing real/synthetic skeleton data is provided.

• Visualize data from NTU RGB+D 120:

python visualize/visualize_skt.py \
    --src_path datasets/ntu120/data2p/xsub.train.h5 \
    --adj_file datasets/ntu120/ntu_adjacency.h5 \
    --save_path real_data_vis/ntu120_2p \
    --save_video 

• Visualize data from BABEL (Please first download the SMPL model (SMPL_NEUTRAL is prefered) from SMPL and put it in datasets/babel/):

python visualize/visualize_skt_smpl.py \
    --src_path datasets/babel/train120.h5 \
    --adj_file datasets/babel/smpl_adjacency.h5 \
    --save_path real_data_vis/babel \
    --save_video 

• Visualize data from GTA Combat Dataset:

python visualize/visualize_skt_gta.py \
    --src_path  datasets/gta/gta_combat_2p.h5 \
    --adj_file datasets/gta/gta_adjacency.h5 \
    --save_path real_data_vis/gta_2p \
    --save_video 

Evaluation

• Train the ST-GCN action recognition model for quantitative evaluation.

# NTU RGB+D 120 (1P)
python main.py recognition -c config/recognition/ntu120_1p.yaml

# BABEL
python main.py recognition_resample -c config/recognition/babel.yaml

# NTU RGB+D 120 (2P)
python main.py recognition -c config/recognition/ntu120_2p.yaml

• Evaluate using the trained ST-GCN action recognition model

# NTU RGB+D 120 (1P)
python main.py evaluator -c config/eval_motion/ntu120_1p.yaml --acc --fid

# BABEL
python main.py evaluator -c config/eval_motion/babel.yaml --acc --fid

# NTU RGB+D 120 (2P)
python main.py evaluator -c config/eval_motion/ntu120_2p.yaml --acc --fid
python main.py evaluator -c config/eval_motion_multip/ntu120_2p.yaml --fid --aggregate    # Aggregate FID

TODO

• Release the code, including the training/evaluating scripts.
• Release the GTA Combat dataset and the visualization script.
• Release the evaluation script for GTA Combat dataset.
• Release the trained models.

Citation

If you find our work useful in your research, please consider citing:

@article{xu2023actformer,
  title={ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation},
  author={Xu, Liang and Song, Ziyang and Wang, Dongliang and Su, Jing and Fang, Zhicheng and Ding, Chenjing and Gan, Weihao and Yan, Yichao and Jin, Xin and Yang, Xiaokang and others},
  journal={{ICCV}},
  year={2023}
}