Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images

[Project] [Paper]

Requirements

• Python (tested on 3.8)
• Pytorch (tested on 1.7)

Installation

Create env

git clone https://github.com/nicolasugrinovic/size_depth_disambiguation.git
cd size_depth_disambiguation
conda create -n sizedepth python=3.8
conda activate sizedepth
pip install -r requirements.txt

Install Detectron2

Install Detectron2 so it can be accesible from the conda environment. The fastest way to install is via pip.

Install Panoptic DeepLab

Go to "external" folder:

cd ./external

and install in this folder the following:

• Panoptic DeepLab

Download Midas-v3 weights

We use Midas-v3 as the depth detector. There is no need for installation as the code used is provided here. However, you need to download the model weights and place them on ./weights folder. The weights to be donwloaded are dpt_large-midas-2f21e586.pt

mkdir ./weights
wget https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt

Install pytorch3d

Install pytorch3d . Though the only function used is load_obj. You may use any other library to load the meshes, just be aware of the vertex order at reading.

Demo

To run the demo, you first need to generate (or precompute) data which is then used by the optimization method. You need to generate initial pose/shape estimations and give them the correct format. Alternatevily, you can download and use this data.

To precompute the data run the following command:

python precompute_estimation_data.py --input_path=./input/coco_demo --output_path=./precomputed_data/coco_demo/ --mode=smpl_reproj --dataset=demo --model_type=dpt_large --config-file ./external/panoptic_deeplab/tools_d2/configs/COCO-PanopticSegmentation/panoptic_deeplab_H_48_os16_mg124_poly_200k_bs64_crop_640_640_coco_dsconv.yaml --opts MODEL.WEIGHTS ./external/panoptic_deeplab/tools_d2/checkpoints/panoptic_deeplab_H_48_os16_mg124_poly_200k_bs64_crop_640_640_coco_dsconv.pth

Change input_path accordingly to where you have the initial input data. This can have any location as long as it has the following structure:

input
|-- data_name
    `-- img_name1.jpg
    `-- img_name1_TRANS_person0.obj
    .
    .
    .
    `-- img_name1_TRANS_personN.obj
    `-- img_name1_3djoints_0.json
    .
    .
    .
    
    `-- img_name1_3djoints_N.json
    `-- img_name2.jpg
    .
    .
    .

This will generate the data inside ./precomputed_data/coco_demo/ folder.

Finally, to run the optimization from our method execute the following command:

python run_optim_demo.py --model_type=dpt_large --input_path=./input/coco_demo --output_path=./output/coco_demo --input=input/coco_demo/*.jpg --mode=smpl_reproj --plane_scale=1.0 --n_iters=2000 --w_ordinal_loss=0 --w_reg_size=0.0 --config-file ./external/panoptic_deeplab/tools_d2/configs/COCO-PanopticSegmentation/panoptic_deeplab_H_48_os16_mg124_poly_200k_bs64_crop_640_640_coco_dsconv.yaml --opts MODEL.WEIGHTS ./external/panoptic_deeplab/tools_d2/checkpoints/panoptic_deeplab_H_48_os16_mg124_poly_200k_bs64_crop_640_640_coco_dsconv.pth

Alternatively, you can run the scripts precompute_data.sh and run_demo.sh found in the scripts folder

Test/Eval

Run eval.py

Citation

If you use this code or ideas from the paper for your research, please cite our paper:

@inproceedings{ugrinovic2021body,
  title={Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images},
  author={Ugrinovic, Nicolas and Ruiz, Adria and Agudo, Antonio and Sanfeliu, Alberto and Moreno-Noguer, Francesc},
  booktitle={2021 International Conference on 3D Vision (3DV)},
  pages={53--63},
  year={2021},
  organization={IEEE}
}