DensePose: Dense Human Pose Estimation In The Wild
Facebook AI Research group presented a paper on pose estimation. That will help Facebook with better understanding of the processed videos.
NEW: DensePose-COCO, a large-scale ground-truth dataset with image-to-surface correspondences manually annotated on 50K COCO images.
Project website: http://densepose.org/
Arxiv: https://arxiv.org/abs/1802.00434
#facebook #fair #cvpr #cv #CNN #dataset
Facebook AI Research group presented a paper on pose estimation. That will help Facebook with better understanding of the processed videos.
NEW: DensePose-COCO, a large-scale ground-truth dataset with image-to-surface correspondences manually annotated on 50K COCO images.
Project website: http://densepose.org/
Arxiv: https://arxiv.org/abs/1802.00434
#facebook #fair #cvpr #cv #CNN #dataset
arXiv.org
DensePose: Dense Human Pose Estimation In The Wild
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We first gather dense...
Transferring Dense Pose to Proximal Animal Classes
Article on how to train DensePose for animals withiout labels
DensePose approach predicts the pose of humans densely and accurately given a large dataset of poses annotated in detail. It's super expensive to collect DensePose annotations for all different classes of animals. So authors show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in DensePose for humans. They propose to utilize the existing annotations of humans and do self-training on unlabeled images of animals.
Link: https://asanakoy.github.io/densepose-evolution/
YouTube: https://youtu.be/OU3Ayg_l4QM
Paper: https://arxiv.org/pdf/2003.00080.pdf
#Facebook #FAIR #CVPR #CVPR2020 #posetransfer #dl
Article on how to train DensePose for animals withiout labels
DensePose approach predicts the pose of humans densely and accurately given a large dataset of poses annotated in detail. It's super expensive to collect DensePose annotations for all different classes of animals. So authors show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in DensePose for humans. They propose to utilize the existing annotations of humans and do self-training on unlabeled images of animals.
Link: https://asanakoy.github.io/densepose-evolution/
YouTube: https://youtu.be/OU3Ayg_l4QM
Paper: https://arxiv.org/pdf/2003.00080.pdf
#Facebook #FAIR #CVPR #CVPR2020 #posetransfer #dl
YouTube
DensePose applied on chimps: comparison of our method before self-training (left) and after (right)
Frame-by-frame predictions produced by our model before (teacher) and after self-training (student).
After self training the 24-class body part segmentation is more accurate and stable.
Project page: https://asanakoy.github.io/densepose-evolution/
After self training the 24-class body part segmentation is more accurate and stable.
Project page: https://asanakoy.github.io/densepose-evolution/