DensePose-PoseTrack

We introduce the DensePose-Posetrack dataset, which consists of videos of multiple persons containing rapid motions, occlusions and scale variations which leads to a very challenging correspondence task. DensePose-PoseTrack will be a part of the ECCV 2018 - POSETRACK CHALLENGE.

Please first follow the INSTALL.md and GETTING_STARTED.md, to install and run the DensePose inference and training. Herein, we provide instructions to download and evaluate on the DensePose-PoseTrack dataset.

Fetch DensePose-PoseTrack dataset

To download the images of the original PoseTrack dataset, please refer to the posetrack webpage: https://posetrack.net. Note that we have used the keypoints provided in the PoseTrack dataset to form the DensePose-PoseTrack dataset. Our dense correspondence annotations are distributed under NonCommercial Creative Commons license.

To downoad, run:

cd $DENSEPOSE/PoseTrack
bash get_DensePose_PoseTrack.sh

This script downloads *.json files that contains all annotations along with files that only contains annotations for images with densepose annotations. The latter is used during evaluation.

Visualization of the DensePose-PoseTrack annotations are demonstrated in the DensePose-PoseTrack-Visualize.ipynb:

Setting-up the PoseTrack dataset.

Create a symlink for the PoseTrack dataset in your datasets/data folder.

ln -s /path/to/posetrack $DENSEPOSE/detectron/datasets/data/posetrack

Create symlinks for the DensePose-PoseTrack annotations

ln -s $DENSEPOSE/PoseTrack/DensePose_PoseTrack/densepose_only_posetrack_train2017.json $DENSEPOSE/detectron/datasets/data/posetrack/
ln -s $DENSEPOSE/PoseTrack/DensePose_PoseTrack/densepose_only_posetrack_val2017.json $DENSEPOSE/detectron/datasets/data/posetrack/
ln -s $DENSEPOSE/PoseTrack/DensePose_PoseTrack/densepose_posetrack_test2017.json $DENSEPOSE/detectron/datasets/data/posetrack/

Your local PoseTrack dataset copy at /path/to/posetrack should have the following directory structure:

posetrack
|_ images
|  |_ <im-folder-1>
|  |_ ...
|  |_ <im-folder-N>.
|_ densepose_only_posetrack_train2017.json
|_ densepose_only_posetrack_val2017.json
|_ densepose_posetrack_test2017.json

Evaluation on DensePose-PoseTrack dataset

To demonstrate the evaluation, we use a DensePose-RCNN with a ResNet-50 trunk that is trained on the DensePose-COCO dataset.

cd $DENSEPOSE
python2 tools/test_net.py \
    --cfg PoseTrack/configs/DensePose_ResNet50_FPN_s1x-e2e.yaml \
    TEST.WEIGHTS https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet50_FPN_s1x-e2e.pkl \
    NUM_GPUS 1

The evaluation of this baseline network should yield Bounding Box AP: 0.4438 and DensePose AP: 0.2698.