The COCO DensePose Challenge is designed to push the state of the art in dense human pose estimation. The task of dense human pose estimation comprises
Schematic representation of the task is given in Figure 1.
Figure 1. Dense human pose estimation involves human body detection, human body segmentation and mapping all pixels that belong to a human body to the 3D surface of the body.This challenge is part of the Joint COCO and LVIS Recognition Challenge Workshop at ECCV 2020. For further details about the joint workshop please visit the workshop website. Please also take a look at the concurrent COCO 2020 Detection, Panoptic and Keypoints challenges.
The COCO DensePose API is used to evaluate results of the DensePose Challenge. The software uses both candidate and reference point correspondences defined by body part index and intrinsic 2D coordinates on the body part, and applies the Average Precision evaluation metric. For more details please check evaluation procedure, results format and upload instructions pages.
Start: Aug. 1, 2019, midnight
Description: The val evaluation server for 2020 COCO DensePose challenge. We encourage use of val to perform validation experiments; for publication, please evaluate your results on test. Results submitted to val will NOT be posted to the public leaderboard on cocodataset.org. JSON file with the results should be named densepose_val_[alg]_results.json, where [alg] with your algorithm name; it should be compressed into a zip file named densepose_val_[alg]_results.zip.
Start: June 13, 2020, midnight
Description: The test evaluation server for 2020 COCO DensePose challenge. We encourage use of test to report evaluation results for publication. You can access the latest public results for comparison at http://cocodataset.org/#densepose-leaderboard. We will migrate results submitted to test regularly to the public leaderboard on cocodataset.org. Please choose "Submit to Leaderboard" if you want your submission to be appeared on our leaderboard. JSON file with the results should be named densepose_test_[alg]_results.json, where [alg] with your algorithm name; it should be compressed into a zip file named densepose_test_[alg]_results.zip.
Aug. 8, 2020, 6:59 a.m.
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