Skip to content

Latest commit

 

History

History
118 lines (92 loc) · 6.28 KB

README.md

File metadata and controls

118 lines (92 loc) · 6.28 KB
Raw

Exemplar Fine-Tuning for 3D Human Pose Fitting Towards In-the-Wild 3D Human Pose Estimation

This repository contains pseudo-GT 3D human pose data produced by Exemplar Fine-Tuning (EFT) method, published in 3DV 2021. The 3D pose data is in the form of SMPL parameters, and this can be used as a supervision to train a 3D pose estimation algiritm (e.g., SPIN or HMR). We found that our EFT dataset is sufficient to build a model that is comparable to the previous SOTA algorithms without using any other indoor 3D pose dataset. See our paper for more details.

This repository also contains the pre-trained 3D pose estimation model trained with our EFT dataset and monocular motion capture demo tools. See README_bodymocap.

News:

  • We have released the EFT fitting codes. See the README_run_eft file:

  • We have released FrankMocap by which you can obtain both 3D body+hand outputs. The body module is the same as this repository's model. We encourage to use FrankMocap for body pose estimation.

Installing Requirements

It is convenient and safe to use conda environment

conda create -n venv_eft python=3.6
conda activate venv_eft
pip install -r requirements.txt

Download EFT Fitting data (json formats)

This repository only provides corresponding SMPL parameters for public 2D keypoint datasets (such as COCO, MPII). You need to download images from the original dataset website.

Run the following script to download our EFT fitting data:

sh scripts/download_eft.sh
  • The EFT data will be saved in ./eft_fit/(DB_name).json. Each json file contains a version EFT fitting for a public dataset.
  • See Data Format for details
  • Currently available EFT fitting outputs:
Dataset Name SampleNum Manual Filtering File Name
COCO2014-12kp 28344 No COCO2014-Part-ver01.json
COCO2014-6kp 79051 No COCO2014-All-ver01.json
COCO2014-Val 10510 Yes COCO2014-Val-ver10.json
MPII 14361 No MPII_ver01.json
PoseTrack 28856 No PoseTrack_ver01.json
LSPet-Train 2946 Yes LSPet_ver01.json
LSPet-Test 2433 Yes LSPet_test_ver10.json
OCHuman-Train 2495 Yes OCHuman_train_ver10.json
OCHuman-Test 1783 Yes OCHuman_test_ver10.json
  • COCO2014-All-ver01.json: COCO 2014 training set by selecting the samples 6 keypoints or more keypoints are annotated.
  • COCO2014-Part-ver01.json: COCO 2014 training set by selecting the sample that 12 limb keypoints or more are annotated.
  • COCO2014-Val-ver10.json: COCO 2014 val set.
  • MPII_ver01.json : MPII Keypoint Dataset
  • PoseTrack_ver01.json : PoseTrack Dataset by selecting the sample that 12 limb keypoints or more are annotated.
  • LSPet: LSPet Dataset
  • OCHuman : OCHuman Dataset
  • Note that the number of samples are fewer than the original sample numbers in each DB, since we automatically (or manually) filtered out bad samples
  • Manual Filtering: Manual quality check and filtering is done to keep high quality results only. See paper for details

Download Other Required Data

  • SMPL Model (Neutral model: basicModel_neutral_lbs_10_207_0_v1.0.0.pkl):

    • Download in the original website. You need to register to download the SMPL data.
    • Put the file in: ./extradata/smpl/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl
    • Copy the smpl pkl file to a different name (SMPL_NEUTRAL.pkl). You need both files:
    cp ./extradata/smpl/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl ./extradata/smpl/SMPL_NEUTRAL.pkl

  • Densepose (optional, for Densepose rendering):

    • Run the following script
        sh scriptsdownload_dp_uv.sh
    • Files are saved in ./extradata/densepose_uv_data/

Download Images from Original Public DB website

Visualize EFT Fitting Results

Run EFT Fitting by yourself

Model Zoo

  • We also share pre-trained models trained with diverse dataset.
  • Plaese see "scripts/download_model_zoo.sh"

Motion Capture Demo

Citation

@inproceedings{joo2020eft,
  title={Exemplar Fine-Tuning for 3D Human Pose Fitting Towards In-the-Wild 3D Human Pose Estimation},
  author={Joo, Hanbyul and Neverova, Natalia and Vedaldi, Andrea},
  booktitle={3DV},
  year={2020}
}

License

CC-BY-NC 4.0. See the LICENSE file.

References

The body mocap code is a modified version of SPIN, and the majority of this code is borrowed from it.