We are working on an automaticĮvaluation server and performance analysis tools based on rich test In the literature we withhold the test annotations to prevent Set we obtained richer annotations including body part occlusions andįollowing the best practices for the performance evaluation benchmarks Preceding and following un-annotated frames. Each image was extracted from a YouTube video and provided with Overall the dataset coversĤ10 human activities and each image is provided with an activity The images were systematically collected using an established The dataset includes aroundĢ5K images containing over 40K people with annotated body
DensePose-RCNN, a variant of Mask-RCNN, to densely regress part-specific UV coordinates within every human region at multiple frames per second.ĭensePose is available under the Creative Commons license on GitHub We’re also releasing performance baselines for multiple pre-trained models alongside with the ground-truth information for DensePose-COCO.MPII Human Pose dataset is a state of the art benchmark for evaluation. DensePose-COCO, a large-scale ground-truth dataset with image-to-surface correspondences manually annotated on 50K COCO images. Learn more about the work in this blog and our CVPR 2018 paper DensePose: Dense Human Pose Estimation In The Wild. The DensePose project addresses this and aims at understanding humans in images in terms of such surface-based models. For these types of tasks, a more complete, surface-based image interpretation is required. Imagine trying on new clothes via a photo or putting costumes on your friend’s photos. This may suffice for applications like gesture or action recognition, but it delivers a reduced image interpretation. 
Research in human understanding aims primarily at localizing a sparse set of joints, like the wrists, or elbows of humans. DensePose, is Facebook’s real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body.
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