The COCO DensePose Task requires dense estimation of human pose in challenging, uncontrolled conditions. The DensePose task involves simultaneously detecting people, segmenting their bodies and mapping all image pixels that belong to a human body to the 3D surface of the body. For full details of this task please see the DensePose evaluation page.
This task is part of the COCO+Mapillary Joint Recognition Challenge Workshop at ICCV 2019. For further details about the joint workshop, as well as new rules regarding technical reports and awards, please visit the workshop page. Please also see the related COCO detection, panoptic and keypoints tasks.
The COCO train, validation, and test sets, containing more than 39,000 images and 56,000 person instances labeled with DensePose annotations are available for download. Annotations on train ( train 1, train 2 ) and val with over 48,000 people are publicly available. Test set with the list of images is also available for download.
Evaluation server for the 2019 task is open.
Vasil Khalidov (Facebook AI Research)
Natalia Neverova (Facebook AI Research)
Riza Alp Güler (Imperial College London / Ariel AI)
Iasonas Kokkinos (UCL / Ariel AI)
Participants are recommended but not restricted to train their algorithms on COCO DensePose train and val sets. The download page has links to all COCO data. When participating in this task, please specify any and all external data used for training in the "method description" when uploading results to the evaluation server. A more thorough explanation of all these details is available on the guidelines page, please be sure to review it carefully prior to participating. Results in the correct format must be uploaded to the evaluation server. The evaluation page lists detailed information regarding how results will be evaluated. Challenge participants with the most successful and innovative methods will be invited to present at the workshop.
We provide extensive API support for the COCO images, annotations, and evaluation code. To download the COCO DensePose API, please visit our GitHub repository. Due to the large size of COCO and the complexity of this task, the process of participating may not seem simple. To help, we provide explanations and instructions for each step of the process: download, data format, results format, upload and evaluation pages. For additional questions, please contact vkhalidov@fb.com and nneverova@fb.com.