ReFit

Code repository for the paper:
ReFit: Recurrent Fitting Network for 3D Human Recovery
Yufu Wang, Kostas Daniilidis
ICCV 2023
[Project Page]

Installation instructions

python3 -m venv refit
source refit/bin/activate
pip install -U pip
pip install -r requirements.txt

Prepare data

There are some few key data you need for the demo, training and evaluation.

1. SMPL: Please donwload SMPL_NEUTRAL/FEMALE/MALE.pkl and put them under data/smpl
2. ReFit weights: download each checkpoint folders and put them under data/pretrain
3. Yolov7: for detection, download this Yolov7 checkpoint and put it under data/pretrain. Be sure to also clone the yolov7 submodule with git clone --recurse-submodules this-repo.

Additionally for training and evaluation, please follow the dataset preparation guide here.

Demo

We provide a few examples in data/examples. Running the following demo will detect each person in each image and then run ReFit for 3D reconstruction. The resulting rendering will be saved.

python demo.py

Training and evaluation

Training: config.yaml, config_bedlam.yaml and config_all.yaml control the dataset composition.

python train.py --cfg configs/config.yaml 

Evaluation:

python scripts/eval.py

Acknowledgements

We benefit greatly the following repos, from which we adapted parts of our code.

• SPIN
• PARE
• PyMAF
• CLIFF
• BEDLAM

Citing

If you find the model and code useful, please consider citing the following paper:

@Inproceedings{wang23refit,
  Title          = {ReFit: Recurrent Fitting Network for 3D Human Recovery},
  Author         = {Yufu Wang and Kostas Daniilidis},
  Booktitle      = {International Conference on Computer Vision},
  Year           = {2023}
}