Towards uncontrained hand-object reconstruction from RGB videos

Demo

Setup

Environment setup

Note that you will need a reasonably recent GPU to run this code.

We recommend using a conda environment:

conda env create -f environment.yml
conda activate phosa16

External Dependencies

Detectron2, NMR, FrankMocap

• Install Detectron2

mkdir -p external
git clone --branch v0.2.1 https://github.com/facebookresearch/detectron2.git external/detectron2
pip install external/detectron2

• Install a slightly modified fast version of Neural Mesh Renderer (NMR) (modification makes this implementation of NMR pytorch 1.6 compatible :) )

mkdir -p external
git clone https://github.com/hassony2/multiperson.git external/multiperson
pip install external/multiperson/neural_renderer
cd external/multiperson/sdf
pip install external/multiperson/sdf

• Install FrankMocap, with a slight twist to return the detected objects
  from Understanding Human Hands in Contact at Internet Scale, Shan et al., CVPR 2020.

mkdir -p external
git clone https://github.com/hassony2/frankmocap.git external/frankmocap
sh scripts/install_frankmocap.sh

Install MANO

Install SMPL-X

Download datasets

HO-3D

Download the dataset following the instructions on the official project webpage.

This code expects to find the ho3d root folder at
local_data/datasets/ho3d

Core50

Running the Code

Check installation

Make sure your file structure after completing all the Setup steps, your file structure in the homan folder looks like this.

# Installed datasets
local_data/
  datasets/
    ho3d/
    core50/
    ShapeNetCore.v2/
    epic/
# Auxiliary data needed to run the code
extra_data/
  # MANO data files
  mano/
    MANO_RIGHT.pkl
    ...
  smpl/
    SMPLX_NEUTRAL.pkl

Start fitting

Core50

Step 1

• Pre-processing images
• Joint optimization with coarse interaction terms

python fit_vid_dataset.py --dataset core50 --optimize_object_scale 0 --result_root results/core50/step1

Step 2

• Joint optimization refinement

python fit_vid_dataset.py --dataset core50 --split test --lw_collision 0.001 --lw_contact 1 --optimize_object_scale 0 --result_root results/core50/step2 --resume results/core50/step1

HO3d

Step 1

• Pre-processing images
• Joint optimization with coarse interaction terms

python fit_vid_dataset.py --dataset ho3d --split test --optimize_object_scale 0 --result_root results/ho3d/step1

Step 2

• Joint optimization refinement

python fit_vid_dataset.py --dataset ho3d --split test --lw_collision 0.001 --lw_contact 1 --optimize_object_scale 0 --result_root results/ho3d/step2 --resume results/ho3d/step1

Acknowledgements

PHOSA

The code for this project is heavily based on and influenced by
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild (PHOSA)] by
Jason Y. Zhang*, Sam Pepose*, Hanbyul Joo, Deva Ramanan, Jitendra Malik, and Angjoo
Kanazawa, ECCV 2020

Consider citing their work !

@InProceedings{zhang2020phosa,
    title = {Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild},
    author = {Zhang, Jason Y. and Pepose, Sam and Joo, Hanbyul and Ramanan, Deva and Malik, Jitendra and Kanazawa, Angjoo},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year = {2020},
}

Funding

This work was funded in part by the MSR-Inria joint lab, the French government
under management of Agence Nationale de la Recherche as
part of the ”Investissements d’avenir” program, reference
ANR19-P3IA-0001 (PRAIRIE 3IA Institute) and by Louis
Vuitton ENS Chair on Artificial Intelligence.

Other references

If you find this work interesting, you will certainly be also interested in the following publication:

• Reconstructing Hand-Object Interactions in the Wild, Cao et al, ICCV 2021

To keep track of recent publications take a look at awesome-hand-pose-estimation by
Xinghao Chen.

License

Note that our code depends on other libraries,
including SMPL, SMPL-X, MANO which each have their own respective licenses that must also be
followed.

GitHub

https://github.com/hassony2/homan