This task aims to solve root-relative 3D multi-person pose estimation. No human bounding box and root joint coordinate groundtruth are used in testing time.
CMU Panoptic is a large scale dataset providing 3D pose annotations (1.5 millions) for multiple people engaging social activities. It contains 65 videos (5.5 hours) with multi-view annotations, but only 17 of them are in multi-person scenario and have the camera parameters. Massively Multiview System * 480 VGA camera views * 30+ HD views * 10 RGB-D sensors * Hardware-based sync * Calibration * Interesting Scenes with Labels Multiple people * Socially interacting groups * 3D body pose * 3D facial landmarks * Transcripts + speaker ID Hardware setup * 480 VGA cameras, 640 x 480 resolution, 25 fps, synchronized among themselves using a hardware clock * 31 HD cameras, 1920 x 1080 resolution, 30 fps, synchronized among themselves using a hardware clock, timing aligned with VGA cameras * 10 Kinect Ⅱ Sensors. 1920 x 1080 (RGB), 512 x 424 (depth), 30 fps, timing aligned among themselves and other sensors 5 DLP Projectors. synchronized with HD cameras Source: Single-Stage Multi-Person Pose Machines Image Source: http://domedb.perception.cs.cmu.edu/
AGORA is a synthetic human dataset with high realism and accurate ground truth. It consists of around 14K training and 3K test images by rendering between 5 and 15 people per image using either image-based lighting or rendered 3D environments, taking care to make the images physically plausible and photoreal. In total, AGORA contains 173K individual person crops. AGORA provides (1) SMPL/SMPL-X parameters and (2) segmentation masks for each subject in images.
The Campus and Shelf datasets were presented in the paper 3D Pictorial Structures for Multiple Human Pose Estimation. The first dataset shows persons walking and talking in front of a building, and the second up to four persons assembling a shelf.
Relative Human (RH) contains multi-person in-the-wild RGB images with rich human annotations, including: Depth layers: relative depth relationship/ordering between all people in the image. Age group classfication: adults, teenagers, kids, babies. Others: Genders, Bounding box, 2D pose.
The Store Dataset is a dataset for estimating 3D poses of multiple humans in real-time. It is captured inside two kinds of simulated stores with 12 and 28 cameras, respectively. Source: https://arxiv.org/abs/2003.03972