/res-loglikelihood-regression

Code for "Human Pose Regression with Residual Log-likelihood Estimation", ICCV 2021 Oral

Primary LanguagePython

Human Pose Regression with Residual Log-likelihood Estimation

[Paper] [arXiv] [Project Page]

Human Pose Regression with Residual Log-likelihood Estimation
Jiefeng Li, Siyuan Bian, Ailing Zeng, Can Wang, Bo Pang, Wentao Liu, Cewu Lu
ICCV 2021 Oral


Regression with Residual Log-likelihood Estimation

TODO

  • Provide minimal implementation of RLE loss.
  • Provide implementation on Human3.6M dataset.
  • Provide implementation on COCO dataset.

Installation

  1. Install pytorch >= 1.1.0 following official instruction.
  2. Install rlepose:
pip install cython
python setup.py develop
  1. Install COCOAPI.
pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
  1. Init data directory:
mkdir data
  1. Download COCO data:
|-- data
`-- |-- coco
    `-- |-- annotations
        |   |-- person_keypoints_train2017.json
        |   `-- person_keypoints_val2017.json
        `-- images
            |-- train2017
            |   |-- 000000000009.jpg
            |   |-- 000000000025.jpg
            |   |-- 000000000030.jpg
            |   |-- ... 
            `-- val2017
                |-- 000000000139.jpg
                |-- 000000000285.jpg
                |-- 000000000632.jpg
                |-- ...

Train from scratch

./scripts/train.sh ./configs/256x192_res50_regress-flow.yaml train_rle

Evaluation

Download the pretrained model from Google Drive.

./scripts/validate.sh ./configs/256x192_res50_regress-flow.yaml ./coco-laplace-rle.pth

Citing

If our code helps your research, please consider citing the following paper:

@inproceedings{li2021human,
    title={Human Pose Regression with Residual Log-likelihood Estimation},
    author={Li, Jiefeng and Bian, Siyuan and Zeng, Ailing and Wang, Can and Pang, Bo and Liu, Wentao and Lu, Cewu},
    booktitle={ICCV},
    year={2021}
}