This repo includes the source code for our BMVC 2019 paper on video-based 3D human pose estimation. We adopt a factorization approach to decompose trajectories along temporal axis and infer the 3D human poses in trajectory space. For details, please read our paper which can be found at https://arxiv.org/abs/1908.08289.
Bibtex:
@inproceedings{lin2019trajectory,
title={Trajectory Space Factorization for Deep Video-Based 3D Human Pose Estimation},
author={Lin, Jiahao and Lee, Gim Hee},
booktitle={BMVC},
year={2019}
}
The code is developed and tested on the following environment
- Python 3.5.2
- PyTorch 0.4.1
- CUDA 9.0
The source code is for training and testing on Human3.6M dataset. Processed data is available here. Unzip and put all files under the data directory as shown below:
data
├── cameras.h5 (human3.6m camera params)
├── train.h5 (human3.6m test data)
├── test.h5 (human3.6m training data)
├── bases.npy (a sample set of svd bases)
└── data_cpn.npz (2d detections)
The data processed in this project are based on the data released by Martinez et al. and Pavllo et al.. Refer to their repos for further information on the data processing.
To train a model, run:
python train.py --bases xxx --input xxx --nframes xxx --nbases xxx --gpu xxx
For example, to train on CPN 2d detections with 8 DCT bases on video sequences of 50 frames:
python train.py --bases dct --input det --nframes 50 --nbases 8 --gpu 0
This will automatically generate an experiment folder under the log directory with name MMDD_HHMMSS_F50_k8_dct_det.
Options to configure the training process:
-b, --bases: trajectory bases used in experiment, could be dct or svd.
-i, --input: input 2d data to train and test the model, could be det for 2d detections or gt for 2d ground truths.
-f, --nframes: specify the number of frames of the video sequences processed in the model.
-k, --nbases: specify the number of bases used in the model, i.e., first k trajectory bases will be used for approximation. k <= f.
-g, --gpu: id of gpu to use.
These options could also be specified in train.py for training and test.py for evaluation.
A model will be evaluated at the end of training. You can also evaluate a pre-trained model with test.py.
Our pre-trained models are available for download. Place the unzipped models under the log directory as shown below:
log
├── 0422_122443_F50_k8_dct_det
│ └── ckpt.pth.tar
└── 0424_175624_F50_k8_dct_gt
└── ckpt.pth.tar
To evaluate on a pretrained model exp_tag, run:
python test.py --exp exp_tag --gpu gpu_id
For example:
python test.py --exp 0422_122443_F50_k8_dct_det --gpu 0