README.md

🤸‍♂️💥🚗 Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective [video]

Dependencies

conda create -n PVCP_env python=3.7
conda activate PVCP_env

# Please install PyTorch according to your CUDA version.
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt

Some of our code and dependencies was adapted from MotionBERT.

🔗 PVCP Dataset

We have provided a special Tool for SMPL annotation: SMPL_Tools.

Download the PVCP Dataset (≈43G). Directory structure:

PVCP
├── annotation
│   ├── dataset_2dpose.json
│   ├── dataset_mesh (coming soon).json
│   ├── mb_input_det_pose.json
│   ├── train_test_seq_id_list.json
│   ├── mesh_det_pvcp_train_release (coming soon).pkl
│   └── mesh_det_pvcp_train_gt2d_test_det2d (coming soon).pkl
├── frame
│   └── image2frame.py
├── image
│   ├── S000_1280x720_F000000_T000000.png
│   ├── S000_1280x720_F000001_T000001.png
│   ├── S000_1280x720_F000002_T000002.png
│   ├── ...
│   └── S208_1584x660_F000207_T042510.png
├── video
│   ├── S000_1280x720.mp4
│   ├── S001_1280x720.mp4
│   ├── S002_1280x720.mp4
│   ├── ...
│   └── S208_1584x660.mp4
└── vis_2dkpt_ann.mp4

For the frame folder, run image2frame.py. The folder structure is as follows:

├── frame
   ├── frame_000000.png
   ├── frame_000001.png
   ├── frame_000002.png
   ├── ...
   └── frame_042510.png

🚩 Stay Tuned For:

• We are working on more refined gesture labeling.
• We will add more types of annotation information.
• ...

🕸️ PPSENet Framework

Project Directory Structure

PVCP
├── checkpoint
├── configs
│   ├── mesh
│   └── pretrain
├── data
│   ├── mesh
│   └── pvcp
├── lib
│   ├── data
│   ├── model
│   └── utils
├── params
├── tools
├── LICENSE
├── README_MotionBERT.md
├── requirements.txt
├── train_mesh_pvcp.py
└── infer_wild_mesh_list.py

Data

1. Download the other datasets here and put them to data/mesh/. We use Human3.6M, COCO, and PW3D for training and testing. Descriptions of the joint regressors could be found in SPIN.
2. Download the SMPL model(basicModel_neutral_lbs_10_207_0_v1.0.0.pkl) from SMPLify, put it to data/mesh/, and rename it as SMPL_NEUTRAL.pkl
3. Download the PVCP dataset and put them to data/pvcp/. mv mesh_det_pvcp_train_release.pkl and mesh_det_pvcp_train_gt2d_test_det2d.pkl to data/mesh/.

• You can also skip the above steps and download our data (include PVCP Dataset) and checkpoint folders directly. Final, data directory structure as follows:

  ├── data
      ├── mesh
      │   ├── J_regressor_extra.npy
      │   ├── J_regressor_h36m_correct.npy
      │   ├── mesh_det_coco.pkl
      │   ├── mesh_det_h36m.pkl
      │   ├── mesh_det_pvcp_train_gt2d_test_det2d.pkl
      │   ├── mesh_det_pvcp_train_release.pkl
      │   ├── mesh_det_pw3d.pkl
      │   ├── mesh_hybrik.zip
      │   ├── smpl_mean_params.npz
      │   └── SMPL_NEUTRAL.pkl
      └── pvcp
          ├── annotation
          │   ├── dataset_2dpose.json
          │   ├── dataset_mesh (coming soon).json
          │   ├── mb_input_det_pose.json
          │   ├── train_test_seq_id_list.json
          │   ├── mesh_det_pvcp_train_release (coming soon).pkl
          │   └── mesh_det_pvcp_train_gt2d_test_det2d (coming soon).pkl
          ├── frame
          ├── image
          └── video
  

Train:

Finetune from a pretrained model with PVCP

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_mesh_pvcp.py \
--config configs/mesh/MB_ft_pvcp.yaml \
--pretrained checkpoint/pretrain/MB_release \
--checkpoint checkpoint/mesh/ft_pvcp_iter3_class0.1_gt_release

Evaluate

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_mesh_pvcp.py \
--config configs/mesh/MB_ft_pvcp.yaml \
--evaluate checkpoint/mesh/ft_pvcp_iter3_class0.1_gt_release/best_epoch.bin 

Test and Demo

python infer_wild_mesh_list.py --out_path output/

👀 Visual

Citation

@inproceedings{
     wang2024pedestriancentric,
     title={Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective},
     author={MeiJun Wang and Yu Meng and Zhongwei Qiu and Chao Zheng and Yan Xu and Xiaorui Peng and Jian Gao},
     booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
     year={2024},
     url={https://openreview.net/forum?id=ldvfaYzG35}
}