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Human Foot Keypoint
2D Keypoints
Pose Estimation
|...
License: CC BY 4.0

Overview

Existing human pose datasets contain limited body part types. The MPII dataset annotates ankles,
knees, hips, shoulders, elbows, wrists, necks, torsos, and head tops, while COCO also includes
some facial keypoints. For both of these datasets, foot annotations are limited to ankle position
only. However, graphics applications such as avatar retargeting or 3D human shape reconstruction
require foot keypoints such as big toe and heel. Without foot information, these approaches
suffer from problems such as the candy wrapper effect, floor penetration, and foot skate. To
address these issues, a small subset of foot instances out of the COCO dataset is labeled
using the Clickworker platform. It is split up with 14K annotations from the COCO training
set and 545 from the validation set. A total of 6 foot keypoints are labeled.
We consider
the 3D coordinate of the foot keypoints rather than the surface position. For instance, for
the exact toe positions, we label the area between the connection of the nail and skin, and
also take depth into consideration by labeling the center of the toe rather than the surface.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{cao2018openpose,
  author = {Zhe Cao and Gines Hidalgo and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  booktitle = {arXiv preprint arXiv:1812.08008},
  title = {Open{P}ose: realtime multi-person 2{D} pose estimation using {P}art {A}ffinity {F}ields},
  year = {2018}
}

License

CC BY 4.0

Data Summary
Type
Image,
Amount
--
Size
18.78GB
Provided by
Carnegie Mellon University
Carnegie Mellon University (CMU) is a private research university based in Pittsburgh, Pennsylvania.
| Amount -- | Size 18.78GB
Human Foot Keypoint
2D Keypoints
Pose Estimation
License: CC BY 4.0

Overview

Existing human pose datasets contain limited body part types. The MPII dataset annotates ankles,
knees, hips, shoulders, elbows, wrists, necks, torsos, and head tops, while COCO also includes
some facial keypoints. For both of these datasets, foot annotations are limited to ankle position
only. However, graphics applications such as avatar retargeting or 3D human shape reconstruction
require foot keypoints such as big toe and heel. Without foot information, these approaches
suffer from problems such as the candy wrapper effect, floor penetration, and foot skate. To
address these issues, a small subset of foot instances out of the COCO dataset is labeled
using the Clickworker platform. It is split up with 14K annotations from the COCO training
set and 545 from the validation set. A total of 6 foot keypoints are labeled.
We consider
the 3D coordinate of the foot keypoints rather than the surface position. For instance, for
the exact toe positions, we label the area between the connection of the nail and skin, and
also take depth into consideration by labeling the center of the toe rather than the surface.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{cao2018openpose,
  author = {Zhe Cao and Gines Hidalgo and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  booktitle = {arXiv preprint arXiv:1812.08008},
  title = {Open{P}ose: realtime multi-person 2{D} pose estimation using {P}art {A}ffinity {F}ields},
  year = {2018}
}

License

CC BY 4.0

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