Unite The People is a dataset for 3D body estimation. The images come from the Leeds Sports Pose dataset and its extended version, as well as the single person tagged people from the MPII Human Pose Dataset. The images are labeled with different types of annotations such as segmentation labels, pose or 3D.
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We provide manual annotations of 14 semantic keypoints for 100,000 car instances (sedan, suv, bus, and truck) from 53,000 images captured from 18 moving cameras at Multiple intersections in Pittsburgh, PA. Please fill the google form to get a email with the download links:
8 PAPERS • 2 BENCHMARKS
UnrealEgo is a dataset that provides in-the-wild stereo images with a large variety of motions for 3D human pose estimation. The in-the-wild stereo images are stereo fisheye images and depth maps with a resolution of 1024×1024 pixels each with 25 frames per second and a total of 450k (900k images) are captured for the dataset. Metadata is provided for each frame, including 3D joint positions, camera positions, and 2D coordinates of reprojected joint positions in the fisheye views.
8 PAPERS • 1 BENCHMARK
PedX is a large-scale multi-modal collection of pedestrians at complex urban intersections. The dataset provides high-resolution stereo images and LiDAR data with manual 2D and automatic 3D annotations. The data was captured using two pairs of stereo cameras and four Velodyne LiDAR sensors.
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Accurate 3D human pose estimation is essential for sports analytics, coaching, and injury prevention. However, existing datasets for monocular pose estimation do not adequately capture the challenging and dynamic nature of sports movements. In response, we introduce SportsPose, a large-scale 3D human pose dataset consisting of highly dynamic sports movements. With more than 176,000 3D poses from 24 different subjects performing 5 different sports activities, SportsPose provides a diverse and comprehensive set of 3D poses that reflect the complex and dynamic nature of sports movements. Contrary to other markerless datasets we have quantitatively evaluated the precision of SportsPose by comparing our poses with a commercial marker-based system and achieve a mean error of 34.5 mm across all evaluation sequences. This is comparable to the error reported on the commonly used 3DPW dataset. We further introduce a new metric, local movement, which describes the movement of the wrist and ankle
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This dataset presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment. All sequences divi
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The dataset is designed specifically to solve a range of computer vision problems (2D-3D tracking, posture) faced by biologists while designing behavior studies with animals.
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DRACO20K dataset is used for evaluating object canonicalization on methods that estimate a canonical frame from a monocular input image.
Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. To tackle this issue with a common benchmark, we introduce the Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality.
1 PAPER • 1 BENCHMARK
A new large-scale dataset that consists of 409 fine-grained categories and 31,881 images with accurate 3D pose annotation.
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.
InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications. It includes 1k videos of diverse avatars performing multiple repetitions of common exercises. It includes significant variation in the environment, lighting conditions, avatar demographics, and movement trajectories. From cadence to kinematic trajectory, each rep is done slightly differently -- just like real humans. InfiniteRep videos are accompanied by a rich set of pixel-perfect labels and annotations, including frame-specific repetition counts.
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