Hand

The 3D Poses in the Wild dataset is the first dataset in the wild with accurate 3D poses for evaluation. While other datasets outdoors exist, they are all restricted to a small recording volume. 3DPW is the first one that includes video footage taken from a moving phone camera. The dataset includes: * 60 video sequences. * 2D pose annotations. * 3D poses obtained with the method introduced in the paper. * Camera poses for every frame in the sequences. * 3D body scans and 3D people models (re-poseable and re-shapeable). Each sequence contains its corresponding models. * 18 3D models in different clothing variations. Source: https://virtualhumans.mpi Image Source: https://virtualhumans.mpi

FreiHAND is a 3D hand pose dataset which records different hand actions performed by 32 people. For each hand image, MANO-based 3D hand pose annotations are provided. It currently contains 32,560 unique training samples and 3960 unique samples for evaluation. The training samples are recorded with a green screen background allowing for background removal. In addition, it applies three different post processing strategies to training samples for data augmentation. However, these post processing strategies are not applied to evaluation samples. Source: Knowledge as Priors: Cross-Modal Knowledge Generalizationfor Datasets without Superior Knowledge Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/FreihandDataset.en.html

DexYCB is a dataset for capturing hand grasping of objects. It can be used three relevant tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand pose estimation. The dataset was built using 20 objects from the YCB-Video dataset, and consists of multiple trials from 10 subjects. For each trial, there is a target object with 2 to 4 other objects placed on a table. The subject is asked to start from a relaxed pose, pick up the target object, and hold it in the air. Some subjects were asked to pretend to hand over the object to someone across from them. Each action is recorded for 3 seconds, repeating the trial 5 times for each target object, each time with a random set of accompanied objects and placement. In total there are 100 trials per subject, and 1,000 trials in total for all subjects. Source: DexYCB: A Benchmark for Capturing Hand Grasping of Objects

Rendered Hand Pose (RHD) is a dataset for hand pose estimation. It provides segmentation maps with 33 classes: three for each finger, palm, person, and background. The 3D kinematic model of the hand provides 21 keypoints per hand: 4 keypoints per finger and one keypoint close to the wrist.

GRAB is a dataset of full-body motions interacting and grasping 3D objects. It contains accurate finger and facial motions as well as the contact between the objects and body. It contains 5 male and 5 female participants and 4 different motion intents. The GRAB dataset also contains binary contact maps between the body and objects. Source: https://github.com/otaheri/GRAB Image Source: https://github.com/otaheri/GRAB

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 InterHand2.6M dataset is a large-scale real-captured dataset with accurate GT 3D interacting hand poses, used for 3D hand pose estimation The dataset contains 2.6M labeled single and interacting hand frames. Source: InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image

The EgoGesture dataset contains 2,081 RGB-D videos, 24,161 gesture samples and 2,953,224 frames from 50 distinct subjects. Source: http://www.nlpr.ia.ac.cn/iva/yfzhang/datasets/egogesture.html Image Source: http://www.nlpr.ia.ac.cn/iva/yfzhang/datasets/egogesture.html

A hand-object interaction dataset with 3D pose annotations of hand and object. The dataset contains 66,034 training images and 11,524 test images from a total of 68 sequences. The sequences are captured in multi-camera and single-camera setups and contain 10 different subjects manipulating 10 different objects from YCB dataset. The annotations are automatically obtained using an optimization algorithm. The hand pose annotations for the test set are withheld and the accuracy of the algorithms on the test set can be evaluated with standard metrics using the CodaLab challenge submission(see project page). The object pose annotations for the test and train set are provided along with the dataset.

The EgoHands dataset contains 48 Google Glass videos of complex, first-person interactions between two people. The main intention of this dataset is to enable better, data-driven approaches to understanding hands in first-person computer vision. The dataset offers * high quality, pixel-level segmentations of hands * the possibility to semantically distinguish between the observer’s hands and someone else’s hands, as well as left and right hands * virtually unconstrained hand poses as actors freely engage in a set of joint activities * lots of data with 15,053 ground-truth labeled hands Source: Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions

COCO-WholeBody is an extension of COCO dataset with whole-body annotations. There are 4 types of bounding boxes (person box, face box, left-hand box, and right-hand box) and 133 keypoints (17 for body, 6 for feet, 68 for face and 42 for hands) annotations for each person in the image. Source: Whole-Body Human Pose Estimation in the Wild Image source: https://arxiv.org/pdf/2007.11858v1.pdf

The SHREC dataset contains 14 dynamic gestures performed by 28 participants (all participants are right handed) and captured by the Intel RealSense short range depth camera. Each gesture is performed between 1 and 10 times by each participant in two way: using one finger and the whole hand. Therefore, the dataset is composed by 2800 sequences captured. The depth image, with a resolution of 640x480, and the coordinates of 22 joints (both in the 2D depth image space and in the 3D world space) are saved for each frame of each sequence in the dataset. Source: Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition Image Source: http://tosca.cs.technion.ac.il/book/shrec.html

Curates a dataset of SMPL-X fits on in-the-wild images. Source: Monocular Expressive Body Regression through Body-Driven Attention

The NVGesture dataset focuses on touchless driver controlling. It contains 1532 dynamic gestures fallen into 25 classes. It includes 1050 samples for training and 482 for testing. The videos are recorded with three modalities (RGB, depth, and infrared). Source: Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition Image Source: Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network

ARCTIC is a dataset of free-form interactions of hands and articulated objects. ARCTIC has 1.2M images paired with accurate 3D meshes for both hands and for objects that move and deform over time. The dataset also provides hand-object contact information.

3D Hand Pose is a multi-view hand pose dataset consisting of color images of hands and different kind of annotations for each: the bounding box and the 2D and 3D location on the joints in the hand. Source: Large-scale Multiview 3D Hand Pose Dataset

Jester Gesture Recognition dataset includes 148,092 labeled video clips of humans performing basic, pre-defined hand gestures in front of a laptop camera or webcam. It is designed for training machine learning models to recognize human hand gestures like sliding two fingers down, swiping left or right and drumming fingers. Source: Gesture Recognition Dataset: Jester

The NYU Hand pose dataset contains 8252 test-set and 72757 training-set frames of captured RGBD data with ground-truth hand-pose information. For each frame, the RGBD data from 3 Kinects is provided: a frontal view and 2 side views. The training set contains samples from a single user only (Jonathan Tompson), while the test set contains samples from two users (Murphy Stein and Jonathan Tompson). A synthetic re-creation (rendering) of the hand pose is also provided for each view. Source: https://jonathantompson.github.io/NYU_Hand_Pose_Dataset.htm Image Source: https://jonathantompson.github.io/NYU_Hand_Pose_Dataset.htm

First-Person Hand Action Benchmark is a collection of RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations. Source: First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations

MSRA Hands is a dataset for hand tracking. In total 6 subjects' right hands are captured using Intel's Creative Interactive Gesture Camera. Each subject is asked to make various rapid gestures in a 400-frame video sequence. To account for different hand sizes, a global hand model scale is specified for each subject: 1.1, 1.0, 0.9, 0.95, 1.1, 1.0 for subject 1~6, respectively. The camera intrinsic parameters are: principle point = image center(160, 120), focal length = 241.42. The depth image is 320x240, each .bin file stores the depth pixel values in row scanning order, which are 320240 floats. The unit is millimeters. The bin file is binary and needs to be opened with std::ios::binary flag. joint.txt file stores 400 frames x 21 hand joints per frame. Each line has 3 * 21 = 63 floats for 21 3D points in (x, y, z) coordinates. The 21 hand joints are: wrist, index_mcp, index_pip, index_dip, index_tip, middle_mcp, middle_pip, middle_dip, middle_tip, ring_mcp, ring_pip, ring_dip, ring_tip, little_mcp, little_pip, little_dip, little_tip, thumb_mcp, thumb_pip, thumb_dip, thumb_tip. The corresponding *.jpg file is just for visualization of depth and ground truth joints. Source: https://jimmysuen.github.io/txt/cvpr14_MSRAHandTrackingDB_readme.txt Image Source: https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Qian_Realtime_and_Robust_2014_CVPR_paper.pdf

We present a comprehensive framework for egocentric interaction recognition using markerless 3D annotations of two hands manipulating objects. To this end, we propose a method to create a unified dataset for egocentric 3D interaction recognition. Our method produces annotations of the 3D pose of two hands and the 6D pose of the manipulated objects, along with their interaction labels for each frame. Our dataset, called H2O (2 Hands and Objects), provides synchronized multi-view RGB-D images, interaction labels, object classes, ground-truth 3D poses for left & right hands, 6D object poses, ground-truth camera poses, object meshes and scene point clouds. To the best of our knowledge, this is the first benchmark that enables the study of first-person actions with the use of the pose of both left and right hands manipulating objects and presents an unprecedented level of detail for egocentric 3D interaction recognition. We further propose the method to predict interaction classes by estimating the 3D pose of two hands and the 6D pose of the manipulated objects, jointly from RGB images. Our method models both inter- and intra-dependencies between both hands and objects by learning the topology of a graph convolutional network that predicts interactions. We show that our method facilitated by this dataset establishes a strong baseline for joint hand-object pose estimation and achieves state-of-the-art accuracy for first person interaction recognition.

The EgoDexter dataset provides both 2D and 3D pose annotations for 4 testing video sequences with 3190 frames. The videos are recorded with body-mounted camera from egocentric viewpoints and contain cluttered backgrounds, fast camera motion, and complex interactions with various objects. Fingertip positions were manually annotated for 1485 out of 3190 frames. Source: Hand Pose Estimation via Latent 2.5D Heatmap Regression Image Source: https://handtracker.mpi-inf.mpg.de/projects/OccludedHands/EgoDexter.htm

The Hands in action dataset (HIC) dataset has RGB-D sequences of hands interacting with objects. Source: Learning joint reconstruction of hands and manipulated objects Image Source: http://files.is.tue.mpg.de/dtzionas/Hand-Object-Capture/

The HO-3D v3 is the version 3 of the HO-3D dataset with more accurate hand-object poses. HO-3D v3 provides more accurate annotations for both the hand and object poses thus resulting in better estimates of contact regions between the hand and the object. The table below shows the statistics of the HO-3D v2 compared to the HO-3D v3 datasets. | Dataset | Train Set | Test Set | #Images | #Objects | #Subjects | Release Date | |-----------|-----------|----------|---------|----------|-----------|--------------| | HO-3D v2 | 66,034 | 11,524 | 10 | 10 | 9 | 9 Jan 2020 | | HO-3D v3 | 83,325 | 20,137 | 10 | 10 | 1 | 1 Jul 2021 | Source: Official Dataset Image Source: Official Dataset

Human3.6M 3D WholeBody (H3WB) is a large scale dataset with 133 whole-body keypoint annotations on 100K images, made possible by a new multi-view pipeline. It is designed for the three new tasks : i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, iii) 3D whole-body pose estimation from a single RGB image. Source: H3WB: Human3.6M 3D WholeBody Dataset and Benchmark Image Source: https://arxiv.org/pdf/2211.15692v1.pdf

The HInt dataset is frequently used as a <b>generalizability benchmark</b> for 3D Hand Reconstruction. It features three data subsets: HInt-NewDays, HInt-VISOR and HInt-Ego4D subsets and it aims to complement existing datasets used for training and evaluation 3D hand pose estimation. HInt annotates 2D keypoint locations and occlusion labels for 21 keypoints on the hand. It is built off of 3 existing datasets (Hands23, Epic-Kitchens VISOR, and Ego4D) and provides annotations for images from the three existing datasets. Source: Reconstructing Hands in 3D with Transformers Image Source: Official Dataset

Includes egocentric videos containing hands in the wild. Source: Analysis of Hand Segmentation in the Wild

The HandNet dataset contains depth images of 10 participants' hands non-rigidly deforming in front of a RealSense RGB-D camera. The annotations are generated by a magnetic annotation technique. 6D pose is available for the center of the hand as well as the five fingertips (i.e. position and orientation of each). Source: Rule Of Thumb: Deep derotation for improved fingertip detection

The SynthHands dataset is a dataset for hand pose estimation which consists of real captured hand motion retargeted to a virtual hand with natural backgrounds and interactions with different objects. The dataset contains data for male and female hands, both with and without interaction with objects. While the hand and foreground object are synthtically generated using Unity, the motion was obtained from real performances as described in the accompanying paper. In addition, real object textures and background images (depth and color) were used. Ground truth 3D positions are provided for 21 keypoints of the hand. Source: Egocentric 6-DoF Tracking of Small Handheld Objects Image Source: https://handtracker.mpi-inf.mpg.de/projects/OccludedHands/SynthHands.htm

• An underwater dataset collected from several field trials within the EU FP7 project “Cognitive autonomous diving buddy (CADDY)”, where an Autonomous Underwater Vehicle (AUV) was used to interact with divers and monitor their activities. - Purpose: Studying and boosting object classification, segmentation and human pose estimation tasks, where divers use the CADDIAN gesture-based language. - Data were recorded in different environmental conditions that cause various image distortions unique to underwater scenarios, i.e., low contrast, color distortion, and haze. - Dataset characteristics (gesture-related): - 9191 annotated stereo pairs were gathered for 16 classes (gesture types), i.e., 18,382 total samples. - 7190 true negative stereo pairs (14,380 samples) that contain background scenery and divers without gesturing.

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The IPN Hand dataset is a benchmark video dataset with sufficient size, variation, and real-world elements able to train and evaluate deep neural networks for continuous Hand Gesture Recognition (HGR). Source: https://github.com/GibranBenitez/IPN-hand

The VIVA challenge’s dataset is a multimodal dynamic hand gesture dataset specifically designed with difficult settings of cluttered background, volatile illumination, and frequent occlusion for studying natural human activities in real-world driving settings. This dataset was captured using a Microsoft Kinect device, and contains 885 intensity and depth video sequences of 19 different dynamic hand gestures performed by 8 subjects inside a vehicle. Source: Short-Term Temporal Convolutional Networks for Dynamic Hand Gesture Recognition Image Source: http://www.site.uottawa.ca/research/viva/projects/hand_detection/index.html

Robot grasping is often formulated as a learning problem. With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning algorithms is becoming more and more popular. An often overlooked question is how to generate the grasps that make up these data sets. In this paper, we review, classify, and compare different grasp sampling strategies. Our evaluation is based on a fine-grained discretization of SE(3) and uses physics-based simulation to evaluate the quality and robustness of the corresponding parallel-jaw grasps. Specifically, we consider more than 1 billion grasps for each of the 21 objects from the YCB data set. This dense data set lets us evaluate existing sampling schemes w.r.t. their bias and efficiency. Our experiments show that some popular sampling schemes contain significant bias and do not cover all possible ways an object can be grasped.

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