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README_run_eft.md

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Running EFT in your machines

Here, we show EFT fitting procedure by using COCO 2014 as example. You may use other dataset similarly.

Additional Setting

  • Download extra data from SPIN:
sh scripts/download_spin_data.sh

You can see the downloaded files in "./extradata/spin/"

  • Download preprocessed pose_regressor models
sh scripts/download_model_zoo.sh
  • Set coco annotation and image files under ./data_sets/coco
.
├── ...
├── data_sets 
│   └── coco          
│        ├── annotations         # the folder with .json files. E.g., person_keypoints_train2014.json
│        └── train2014           # the folder with image files
└── ...

Note that you may use symbolic links to setup the folder structure

Run preprocessing

  • This process converts the raw 2D annotation data into a unified format (in npz)
  • Run the following
python -m eft.db_processing.coco
  • The output is saved in "./preprocessed_db/coco_2014_train_12kp.npz"

Run EFT Fitting

  • Run the following
python -m demo.eftFitting

  • You will see a GUI window to see EFT process.

  • Use mouse control to see the 3D in other views (see below about the key information)

  • Toggle "C" in 3D Visualizer window to visualize 3D in image cooridnate

  • Press "q" in 3D Visualizer window to move to the next example

  • Other key information:

    • mouse left + move: view change
    • mouse right + move: zoom in/out
    • shift + mouse left + move: pan
    • 'C': toggle between 3D view and image view
    • 'q': go to the next sample
    • 'w': toggle between solid mesh and wire-frame mesh
    • 'j': on/off for 3d skeleton
    • 'm': on/off for 3d mesh
    • 'f': on/off for floor

EFT outputs

  • The EFT output is saved in "./eft_out" as PKL format.
  • You can disable "--bDebug_visEFT" in eftFitting.py if you only want to get EFT outputs without visualization.

License

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