Pytorch implementation of paper "T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations"
[Project Page] [Paper] [Notebook Demo] [HuggingFace] [Space Demo] [T2M-GPT+]
If our project is helpful for your research, please consider citing :
@inproceedings{zhang2023generating,
title={T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations},
author={Zhang, Jianrong and Zhang, Yangsong and Cun, Xiaodong and Huang, Shaoli and Zhang, Yong and Zhao, Hongwei and Lu, Hongtao and Shen, Xi},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023},
}
Text: a man steps forward and does a handstand. | ||||
---|---|---|---|---|
GT | T2M | MDM | MotionDiffuse | Ours |
Text: A man rises from the ground, walks in a circle and sits back down on the ground. | ||||
GT | T2M | MDM | MotionDiffuse | Ours |
Our model can be learnt in a single GPU V100-32G
conda env create -f environment.yml
conda activate T2M-GPT
The code was tested on Python 3.8 and PyTorch 1.8.1.
bash dataset/prepare/download_glove.sh
We are using two 3D human motion-language dataset: HumanML3D and KIT-ML. For both datasets, you could find the details as well as download link [here].
Take HumanML3D for an example, the file directory should look like this:
./dataset/HumanML3D/
├── new_joint_vecs/
├── texts/
├── Mean.npy # same as in [HumanML3D](https://github.com/EricGuo5513/HumanML3D)
├── Std.npy # same as in [HumanML3D](https://github.com/EricGuo5513/HumanML3D)
├── train.txt
├── val.txt
├── test.txt
├── train_val.txt
└── all.txt
We use the same extractors provided by t2m to evaluate our generated motions. Please download the extractors.
bash dataset/prepare/download_extractor.sh
The pretrained model files will be stored in the 'pretrained' folder:
bash dataset/prepare/download_model.sh
If you want to render the generated motion, you need to install:
sudo sh dataset/prepare/download_smpl.sh
conda install -c menpo osmesa
conda install h5py
conda install -c conda-forge shapely pyrender trimesh mapbox_earcut
A quick start guide of how to use our code is available in demo.ipynb
Note that, for kit dataset, just need to set '--dataname kit'.
The results are saved in the folder output.
python3 train_vq.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 VQVAE
The results are saved in the folder output.
python3 train_t2m_trans.py
--exp-name GPT
--batch-size 128
--num-layers 9
--embed-dim-gpt 1024
--nb-code 512
--n-head-gpt 16
--block-size 51
--ff-rate 4
--drop-out-rate 0.1
--resume-pth output/VQVAE/net_last.pth
--vq-name VQVAE
--out-dir output
--total-iter 300000
--lr-scheduler 150000
--lr 0.0001
--dataname t2m
--down-t 2
--depth 3
--quantizer ema_reset
--eval-iter 10000
--pkeep 0.5
--dilation-growth-rate 3
--vq-act relu
python3 VQ_eval.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 TEST_VQVAE
--resume-pth output/VQVAE/net_last.pth
Follow the evaluation setting of text-to-motion, we evaluate our model 20 times and report the average result. Due to the multimodality part where we should generate 30 motions from the same text, the evaluation takes a long time.
python3 GPT_eval_multi.py
--exp-name TEST_GPT
--batch-size 128
--num-layers 9
--embed-dim-gpt 1024
--nb-code 512
--n-head-gpt 16
--block-size 51
--ff-rate 4
--drop-out-rate 0.1
--resume-pth output/VQVAE/net_last.pth
--vq-name VQVAE
--out-dir output
--total-iter 300000
--lr-scheduler 150000
--lr 0.0001
--dataname t2m
--down-t 2
--depth 3
--quantizer ema_reset
--eval-iter 10000
--pkeep 0.5
--dilation-growth-rate 3
--vq-act relu
--resume-trans output/GPT/net_best_fid.pth
You should input the npy folder address and the motion names. Here is an example:
python3 render_final.py --filedir output/TEST_GPT/ --motion-list 000019 005485
We appreciate helps from :