Code repository for the paper:
ReFit: Recurrent Fitting Network for 3D Human Recovery
Yufu Wang, Kostas Daniilidis
ICCV 2023
[Project Page]
python3 -m venv refit
source refit/bin/activate
pip install -U pip
pip install -r requirements.txt
There are some few key data you need for the demo, training and evaluation.
- SMPL: Please download SMPL_NEUTRAL/FEMALE/MALE.pkl and put them under
data/smpl
- ReFit weights: download each checkpoint folders and put them under
data/pretrain
- Yolov7: for detection, download this Yolov7 checkpoint and put it under
data/pretrain
. Be sure to also clone the yolov7 submodule withgit clone --recurse-submodules this-repo
.
Additionally for training and evaluation, please follow the dataset preparation guide here.
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: 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
We benefit greatly the following repos, from which we adapted parts of our code.
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}
}