DJ-RN

As a part of HAKE project (HAKE-3D). Code for our CVPR2020 paper "Detailed 2D-3D Joint Representation for Human-Object Interaction".

News: (2020.6.16) Our larger version HAKE-Large (>120K images, activity and part state labels) is released!

• Paper is here: arXiv
• Single-view human detailed shape reconstruction, 2D & 3D pose and detailed shape from OpenPose and SMPLify-X.
• Interacted object 3D location-size recovering as hollow sphere.
• 2D-3D joint human body part attention.
• Multi-modal representation for HOI detection.

Ambiguous-HOI

This repo contains the code for the proposed Ambiguous-HOI dataset.

To collect the data set, run

bash script/Generate_Ambiguous_HOI.sh

After running this, the data and code related to Ambiguous-HOI would be organized as:

DJ-RN
├──Data
    ├── Ambiguous_HOI
        └── ...                  # The jpeg image files
    └── Test_ambiguous.pkl       # Person and object boudning boxes used by DJ-RN
└── -Results
    ├── gt_ambiguous_hoi_new.pkl # Annotation file
    └── Evaluate_HICO_DET.py     # Evaluation script

Run python ./-Results/Evaluate_ambiguous.py <your detection file> <path to save the result> to evaluate the performace. The detection file should be a pickle file with format of

{'xxx.jpg': # image file name
    [
        [x1, y1, x2, y2],        # Human bounding box
        [x1, y1, x2, y2],        # Object bounding box
        x,                       # Object category, 1~80
        np.array([xx, ..., xx]), # HOI detection score, np.array with size of (600,)
        x,                       # Human detection confidence
        x,                       # Object detection confidence
    ], 
    ...
}

Results on HICO-DET and Ambiguous-HOI

Our results on HICO-DET dataset, using object detections from iCAN (COCO pre-trained detector)

Our results on Ambiguous-HOI

Getting Started

Installation

1. Clone this repository.

2. Download HICO-DET dataset and detection files. The detection results (person and object boudning boxes) are collected from: iCAN.

bash script/Download_HICO-DET.sh

3. Generate Ambiguous-HOI dataset.

bash script/Generate_Ambiguous_HOI.sh

4. Download SMPL-X model referring to this link, and unzip and organize the directory structure as follows:

models
├── smplx
    ├── SMPLX_FEMALE.npz
    ├── SMPLX_FEMALE.pkl
    ├── SMPLX_MALE.npz
    ├── SMPLX_MALE.pkl
    ├── SMPLX_NEUTRAL.npz
    └── SMPLX_NEUTRAL.pkl

models/smplx will be referred to as smplx_path in the following instruction.

5. Download pretrained weight of our model. (Optional)

bash script/Download_weight.sh

6. Download inference results of our model. (Optional)

bash script/Download_result.sh

Data generation

1. Run OpenPose for the images in the dataset. For HICO-DET, the OpenPose result could be downloaded from this link.

2. Create the environment used for this phase.

conda create --name DJR-data --file requirements_data.txt
conda activate DJR-data

3. Filter the OpenPose result.

python script/filter_pose.py --ori <path to your OpenPose result> --fil <path to save the filtered result>

4. Run SMPLify-X on the dataset with the filtered pose.

5. Assign the SMPLify-X results to the training and testing data.

python script/assign_pose_GT.py --pose <path to your pose used for SMPLify-X> --res <path to your SMPLify-X result>
python script/assign_pose_Neg.py --pose <path to your pose used for SMPLify-X> --res <path to your SMPLify-X result>
python script/assign_pose_test.py --pose <path to your pose used for SMPLify-X> --res <path to your SMPLify-X result>

6. Generate 3D spatial configuration.

python script/generate_3D_obj_GT.py --smplx_path <path of the smplx model> --res <path to your SMPLify-X result> --img_path <path to your HICO train image>  --save_obj_path <path to save your object mesh and pkl>  
python script/generate_3D_obj_Neg.py --smplx_path <path of the smplx model> --res <path to your SMPLify-X result> --img_path <path to your HICO train image>  --save_obj_path <path to save your object mesh and pkl>  
python script/generate_3D_obj_test.py --smplx_path <path of the smplx model> --res <path to your SMPLify-X result> --img_path <path to your HICO test image>  --save_obj_path <path to save your object mesh and pkl>  
python script/rotate_sampling_GT.py --smplx_path <path of the smplx model> --res <path to your SMPLify-X result> --obj_path <path to your object vertexs>  --save_path <path to save the spatial configuration>
python script/rotate_sampling_Neg.py --smplx_path <path of the smplx model> --res <path to your SMPLify-X result> --obj_path <path to your object vertexs>  --save_path <path to save the spatial configuration>
python script/rotate_sampling_test.py --smplx_path <path of the smplx model> --res <path to your SMPLify-X result> --obj_path <path to your object vertexs>  --save_path <path to save the spatial configuration>

7. Transfer the results to fit in Python 2.7

python script/transfer_py3-py2.py --res <Path to your SMPLify-X result>

3D Human-Object Interaction Volume Generation and Visualization

We provide a Jupyter Notebook demo for quick start in script/Demo.ipynb.

Extract feature using PointNet

1. Clone the PointNet repo (https://github.com/charlesq34/pointnet.git), and copy the necessary files.

git clone https://github.com/charlesq34/pointnet.git
cp Feature_extraction/Feature_extraction.py pointnet/Feature_extraction.py
cp Feature_extraction/pointnet_hico.py pointnet/models/pointnet_hico.py

2. Install PointNet following instruction.

3. Extract feature

cd pointnet
python script/Download_data.py 1l48pyX-9FWFMNuokdbBp6KshKSDBihAe Feature_extraction.tar
tar -xvf Feature_extraction.tar
python Feature_extraction.py --input_list script/vertex_path_GT.txt --model_path ../Feature_extraction/model_10000.ckpt
python Feature_extraction.py --input_list script/vertex_path_Neg.txt --model_path ../Feature_extraction/model_10000.ckpt
python Feature_extraction.py --input_list script/vertex_path_Test.txt --model_path ../Feature_extraction/model_10000.ckpt

Experiments with our model

1. Create the environment used for this phase.

conda create --name DJR --file requirements_DJR.txt
conda activate DJR

2. Train on HICO-DET

python tools/Train_HICO_DET_DJR.py --model <your model name> --num_iteration 400000

3. Test and generate detection file on HICO-DET

python tools/Test_HICO_DET_DJR.py --model <your model name> --iteration 400000
python ./-Results/Generate_detection.py --model <your test output directory, under ./-Results by default>

4. To evaluate the performance of our model on HICO-DET, run

python ./-Results/Evaluate_HICO_DET.py --file ./-Results/Detection_400000_DJR.pkl

5. Test and generate detection file on Anbiguous-HOI

python tools/Test_ambiguous_DJR.py --model <your model name> --iteration 400000

6. To evaluate the performance of our model on Ambiguous-HOI, run

python ./-Results/Evaluate_ambiguous.py ./-Results/400000_DJR_ambiguous.pkl DJR_ambiguous/

Citation

If you find our work useful, please consider citing:

@inproceedings{li2020detailed,
title={Detailed 2D-3D Joint Representation for Human-Object Interaction},
author={Li, Yong-Lu and Liu, Xinpeng and Lu, Han and Wang, Shiyi and Liu, Junqi and Li, Jiefeng and Lu, Cewu},
booktitle={CVPR},
year={2020}
}

TODOS

• [x] Ambiguous-HOI data and evaluation
• [x] Full model
• [x] 3D human-object generation and visualization
• [ ] Lite model

Acknowledgement

Some of the codes are built upon Interactiveness.

If you get any problems or if you find any bugs, don't hesitate to comment on GitHub or make a pull request!

DJ-RN is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, please drop an e-mail. We will send the detail agreement to you.