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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 The IPN Hand dataset contains more than 4,000 gesture instances and 800,000 frames from 50 subjects . We design 13 static and dynamic gestures for interaction with touchless screens. Compared to other publicly available hand gesture datasets, IPN Hand includes the largest number of continuous gestures per video, and the largest speed of intra. Hand Datasets. [ Sorting Controls ] Datasets are collections of data. BioGPS has thousands of datasets available for browsing and which can be easily viewed in our interactive data chart . Learn more. ‹‹ previous 1 2 3 next ››. Displaying datasets 1 - 10 of 30 in total. View Dataset Dataset Name: TU Berlin - IJRR 2015 Dataset 1 (June 2015) Research Group: TUB Hand Type: Human Hand Data Type: Human Motion, Human Postures Data Structure: Joint Sensor Raw Data Data Format: .csv Sampling Rate: >=100 Hz (100 Hz) Action Type: Reach and Grasp, Static Grasps Objects Type: Real Objects Kin. Model #DOFs: >20 (23) [ The Poker Hand dataset [1] has two properties that makes it particular challenging for classification algorithms: it contains only categorical features (suite and rank of a card) and it's extremely imbalanced (2 out of 10 classes constitute 90% of the samples). This makes it an interesting dataset for studying an

EgoHands: A Dataset for Hands in Complex Egocentric

PALM is a pretrained anime hand detector/localization neural network, and 3 sets of accompanying anime hand datasets: A dataset of 5,382 anime-style Danbooru2019 images annotated with the locations of 14,394 hands. This labeled dataset is used to train a YOLOv3 model to detect hands in anime. A second dataset of 96,534 hands cropped from the. The only code added in this version is ./data/hand_data.py. Rest of the code runs in the same way as the original version. To set up the environment (or to run UnityEyes dataset), please follow the instructions in this link. ###Notes -NYU hand dataset is preprocessed (e.g. background removed) -Image size set to 128x128 Today, the problem is not finding datasets, but rather sifting through them to keep the relevant ones. Well, we've done that for you right here. Below, you'll find a curated list of free datasets for data science and machine learning, organized by their use case. You'll find both hand-picked datasets and our favorite aggregators Context. Hand gesture recognition database is presented, composed by a set of near infrared images acquired by the Leap Motion sensor. Content. The database is composed by 10 different hand-gestures (showed above) that were performed by 10 different subjects (5 men and 5 women) The EgoHands dataset is a collection of 4800 annotated images of human hands from a first-person view originally collected and labeled by Sven Bambach, Stefan Lee, David Crandall, and Chen Yu of Indiana University. The dataset was captured via frames extracted from video recorded through head-mounted cameras on a Google Glass headset while.

The TV-Hand dataset contains hand annotations for 9.5K image frames extracted from the ActionThread dataset. The COCO-Hand dataset contains annotations for 25K images of the Microsoft's COCO dataset. Some images and annotations from the TV-Hand data. Some images and annotations from the COCO-Hand data. Citation. If you use the code or datasets. Data Set Information: Each record is an example of a hand consisting of five playing cards drawn from a standard deck of 52. Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. There is one Class attribute that describes the Poker Hand 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)

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 dataset is created by sampling images and sequences from BigHand2.2M [2] and First-Person Hand Action (FHAD) [3] datasets. Both datasets are fully annotated (21-joints) using an automatic annotating system with six 6D magnetic sensors and inverse kinematics The data set consists of 900 image sequences of 9 gesture classes, which are defined by 3 primitive hand shapes and 3 primitive motions (see Figure 1). Therefore, the target task for this data set is to classify different shapes as well as different motions at a time Multivariate, Text, Domain-Theory . Classification, Clustering . Real . 2500 . 10000 . 201

the IPN Hand dataset demonstrate that it can be used as a benchmark for the data-hungry 3D-CNN methods, which may help the community to step forward in the continuous HGR. II. R ELATED W ORKS A. Datasets for continuous HGR Existing continuous HGR datasets differ by factors such a The data set contains images of hand-written digits: 10 classes where each class refers to a digit. Preprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are. Original Dataset. Normalized Dataset. These are normalized versions of these datasets, so that the numerical values are between 0 and 1. With the Poker-Hand dataset, the cards are not ordered, i.e. a hand can be represented by any permutation, which makes it very hard for propositional learners, especially for linear ones Modelling hand kinematics is a challenging problem, crucial for several domains including robotics, 3D modelling, rehabilitation medicine and neuroscience. Currently available datasets are few and. FreiHAND Dataset. News: An extended version of this dataset with calibration and multiple-views is released in HanCo. In our recent publication we presented the challenging FreiHAND dataset, a dataset for hand pose and shape estimation from single color image, which can serve both as training and benchmarking dataset for deep learning algorithms. It contains 4*32560 = 130240 training and 3960.

The IPN Hand Dataset IPN Han

Hand Datasets BioGP

Included in this dataset are the EMG signals from 8 extrinsic muscles along the forearm, and as much as 23 joint markers attached on the hand obtained from 10 subjects. More description about the experimental protocol, signal processing methods and equipments used are described in the paper below 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)

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About the dataset. This dataset was used to build the real-time, gesture recognition system described in the CVPR 2017 paper titled A Low Power, Fully Event-Based Gesture Recognition System.. The data was recorded using a DVS128. The dataset contains 11 hand gestures from 29 subjects under 3 illumination conditions and is released under a. Overview. We introduce a comprehensive dataset of hand images collected from various different public image data set sources as listed in Table 1. A total of 13050 hand instances are annotated. Hand instances larger than a fixed area of bounding box (1500 sq. pixels) are considered 'big' enough for detections and are used for evaluation Hand Geometry Dataset. The right hand image is captured using an IR Recognition Systems HandKey II biometric device. A Matlab program is used to read the top view image of the hand from the RSI to the computer. The size of the image is 20 KB, 660×768 and 2KB for the .log file. The subject is first enrolled into the system using a seven digit. Welcome to the 11k Hands dataset, a collection of 11,076 hand images (1600 x 1200 pixels) of 190 subjects, of varying ages between 18 - 75 years old.Each subject was asked to open and close his fingers of the right and left hands. Each hand was photographed from both dorsal and palmar sides with a uniform white background and placed approximately in the same distance from the camera

hand detection dataset, yolo hand detection dataset, hand object detection dataset, hand keypoint detection dataset, cmu hand keypoint detection dataset, hand pose estimation dataset, ego2hands a dataset for egocentric two-hand segmentation and detection, hand gesture detection dataset. May 31, 2013 — Toward a 3D Body Part Detection Video. Pisharady and Serbeck 18 reported a comprehensive review of all available vision-based hand gesture datasets, and recently, a dataset of continuous-wave radar datasets for vital signs and.

Crowd of People Raising Their Hands | Pew Research Center

Estimating 3D hand pose from single RGB images is a highly ambiguous problem that relies on an unbiased training dataset. In this paper, we analyze cross-dataset generalization when training on existing datasets. We find that approaches perform well on the datasets they are trained on, but do not generalize to other datasets or in-the-wild. We use images from deeplearning.ai's SIGNS dataset that you have used in one of Course 2 's programming assignment. Each image from this dataset is a picture of a hand making a sign that represents a number between 1 and 6. It is 1080 training images and 120 test images. In our example, we use images scaled down to size 64x64

Datasets HandCorpu

Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine. The hand pose datasets released so far present some issues that make them impossible to use on deep learning methods such as the few number of samples, high-level abstraction annotations or samples consisting in depth maps. In this work, we introduce a multiview hand pose dataset in which we provide color images of hands and different kind of. dataset, Partial occlusion, RGB hand-pose reconstruction: Abstract: Recognizing the pose of hands matters most when hands are interacting with other objects. To understand how well both machines and humans perform on single-image 2D hand-pose reconstruction from RGB images, we collected a challenging dataset of hands interacting with 148 objects

  1. Datasets were generated with Blender 2.74 using a detailed, realistic 3D model of a human adult male hand. The model was rigged using a hand skeleton with four bones per finger, reproducing the distal, intermediate, and proximal phalanges, as well as the metacarpals. The thumb finger had no intermediate phalanx and was controlled with three bones
  2. The hand pose dataset. This is description of our hand-pose dataset which was used to train and test the hand-pose identification in our paper Learning the signatures of the human grasp using a scalable tactile glove.. The dataset is provided for non-commercial use only
  3. Summarized basic statistics of the proposed dataset. The distribution of hand skin colors in the dataset is shown in top. The number of hand images for each hand skin color category is written in the top right. We performed skin detection using Conaire et al.'s method . In bottom of this figure, we show the number of subjects, hand images.
  4. hand model within a deep learning framework yields state-of-the-art performance in 3D pose prediction from images on standard benchmarks, and produces geometrically valid and plausible 3D reconstructions. Additionally, we show that training with weak supervision in the form of 2D joint annotations on datasets of images in the wild, in conjunc
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dataset created for recognizing air signaling gestures. Finally, the BIGHands dataset [29] is a large-scale image dataset of hand poses, it is rich in joint annotation and hand pose variation but does not directly represent gestures. Ta-ble 2 shows a comparison between the most related gesture video datasets =====Image datasets ===== ***Dataset for Natural Images***** ImageNet ()ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. Currently we have an average of over five hundred images per node. The creators of the dataset hope ImageNet will become a useful.

Kinect Numbers and Letters Hand Gestures Datasets Description. The Letters and Numbers Hand Gestures (LNHG) Database is a small dataset intended for gesture recognition. The dataset contains 36 classes. The 36 classes consist of the arabic digits (0, 1,. I captured 78 images from my hand showing 4 different gestures and they are split in 4 folders. I crop some of the images so they are better fit for training our model later. All of the training (prepared) images are stored in dataset folder. left — contains 27 images of hand pointing left; right — contains 24 images of hand pointing. The hand gesture recognition dataset is presented, composed by a set of near infrared images acquired by the Leap Motion Sensor. The database is composed of 10 different hand-gestures that were performed by 10 different subjects (5 men and 5 women). And there are total 40000 images in total MNIST is one of the most popular deep learning datasets out there. It's a dataset of handwritten digits and contains a training set of 60,000 examples and a test set of 10,000 examples. It's a good database for trying learning techniques and deep recognition patterns on real-world data while spending minimum time and effort in data. The previous dataset based on age 16+ denominators has been uploaded as an archived table. Starting on May 29, 2021 the methodology for calculating on-hand inventory in the shipped/delivered/on-hand dataset has changed. Please see the accompanying data dictionary for details. In addition, this dataset is now down to the ZIP code level

Anime Crop Datasets: Faces, Figures, & Hands · Gwern

The Poker Hand dataset [Cattral et al., 2007] is publicly available and very well-documented at the UCI Machine Learning Repository [Dua et al., 2019]. [Cattral et al., 2007] described it as: Found to be a challenging dataset for classification algorithms. It is an 11-dimensional dataset with 25K samples for training and over 1M samples for. Represents a resource for exploring, transforming, and managing data in Azure Machine Learning. A Dataset is a reference to data in a Datastore or behind public web urls. For methods deprecated in this class, please check AbstractDataset class for the improved APIs. The following Datasets types are supported: TabularDataset represents data in a tabular format created by parsing the provided. interactions. Some datasets are dedicated to 3D hand pose capture [7, 33]. The Finger Motion (FM) [15] dataset is closest to ours in terms of its content, as it contains full body and hand motions in conversational settings. However, the dataset contains only scripted motions by actors and has only single-person data without audio

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GitHub - shinseung428/simGAN_NYU_Hand: simGAN NYU Hand Datase

BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019) Hand Gesture Classification using Python Code . In the Hand Gesture Classification, we used a dataset that contains images of different hand gestures, such as a fist, palm, showing the thumb, and others which can be further useful to show counts from 1 to 5 with these hand gestures Datasets. The Science On a Sphere® Dataset Catalog is comprised of datasets from NOAA, NASA, universities, science centers and other organizations. Each dataset entry includes a description of the dataset, a picture, a video, notable features, relevant links, and source information As far as we are aware, our dataset is the first to focus on hand pose estimation across multiple subjects and multiple cluttered scenes. This is important, because any practical application must handle diverse subjects, scenes, and clutter. 1869. Figure 2. Our new test data challenges methods with clutter, ob In this step-by-step tutorial, you'll learn how to start exploring a dataset with Pandas and Python. You'll learn how to access specific rows and columns to answer questions about your data. You'll also see how to handle missing values and prepare to visualize your dataset in a Jupyter notebook

Datasets for Data Science and Machine Learnin

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Hand Gesture Recognition Database Kaggl

This is the first dataset containing 4 cameras images for hand-gesture in contrast with the rest pubic datasets. • Hand gesture recognition might be used from this dataset in supervised and semi-supervised learning context. • This dataset can be applied to study the hand-gesture recognition problems under multiple views An experimental evaluation using several well-established vein recognition schemes on a dataset acquired with the proposed capturing device confirms its good image quality and competitive recognition performance. This challenging dataset, which is one of the first publicly available contactless finger and hand vein datasets, is published as well A validation dataset is a sample of data held back from training your model that is used to give an estimate of model skill while tuning model's hyperparameters. The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of th

EgoHands Object Detection Dataset - Roboflo

  1. Hand Shape Datasets. We provide hand data sets used in the following paper: Quan Yuan, Ashwin Thangali, Vitaly Ablavsky and Stan Sclaroff , Multiplicative Kernels: Object Detection, Segmentation and Pose Estimation, CVPR, 2008. Data. Hand shape with two parameters (about 3400 images
  2. @inproceedings {zimmermann2019freihand, title = {Freihand: A dataset for markerless capture of hand pose and shape from single rgb images}, author = {Zimmermann, Christian and Ceylan, Duygu and Yang, Jimei and Russell, Bryan and Argus, Max and Brox, Thomas}, booktitle = {Proceedings of the IEEE International Conference on Computer Vision}, pages = {813--822}, year = {2019}
  3. Search Datasets. Home; Organizations; WAPOR (FAO) Hand symbol; Hand symbol. Followers 0. Organization. WAPOR (FAO) There is no description for this organization. Social. Twitter; Facebook; License. Against DRM. Dataset; Groups; Activity Stream; Hand symbol For wwweek data story. Data and Resources. Artboard 2@3x.png PNG. Explore Preview.
  4. The NUS hand posture datasets I & II. Data Set I . The NUS hand posture dataset I consists 10 classes of postures, 24 sample images per class, which are captured by varying the position and size of the hand within the image frame. Both greyscale and color images are available (160×120 pixels)
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A new and challenging dataset for single-image RGB hand-pose reconstruction Data coming soon! Battushig Myanganbayar. MIT Cristina Mata MIT Gil Dekel MIT Boris Katz MIT Guy Ben-Yosef; MIT Andrei Barbu Corresponding author abarbu@mit.edu; Published in ACCV 2018. A preview at the ECCV 2018 HANDS Workshop:. 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. 3D hand pose estimation datasets. Table 1 shows speci cation of existing 3D hand pose datasets and the proposed InterHand2.6M. Compared with depth-based 3D hand pose estimation datasets [4,29,33,36,44], existing RGB-based datasets [19,28,45,46] have very limited number of frames and subjects becaus Dataset contains 9 hours of motion capture data, 17 hours of video data from 4 different points of view (including one hand-held camera), and 6.6 hours of IMU data. 2020 MoVi is the first human motion dataset to contain synchronized pose, body meshes and video recordings

GitHub - SupreethN/Hand-CNN: ICCV 2019, Hand Detectio

In this paper we introduce a large-scale hand pose dataset, collected using a novel capture method. Existing datasets are either generated synthetically or captured using depth sensors: synthetic datasets exhibit a certain level of appearance difference from real depth images, and real datasets are limited in quantity and coverage, mainly due to the difficulty to annotate them. We propose a. This dataset contains healthy hands free of musculoskeletal trauma or diseases. The female hand (Subject 02) has a minor localized swelling (about 1cm across) on the last link of the middle finger (pre-existing, temporary, and unrelated to our procedures and scans) MINIST dataset is widely used dataset in machine learning for handwritten recognition, image classification and many more. The MNIST dataset is short form for the Modified National Institute of Standards and Technology dataset. It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9 Hand Keypoint Dataset Page has been added. More data will be coming soon. Jun. 2017 : We organize a tutorial in conjunction with CVPR 2017: DIY A Multiview Camera System: Panoptic Studio Teardown Jun. 2017 : Hand keypoint detection and reconstruction paper will be presented in CVPR 2017: Project page. Dec. 2016 : Panoptic Studio is featured.

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UCI Machine Learning Repository: Poker Hand Data Se

Hand musculoskeletal models provide a valuable insight into the loads withstood by the upper limb; however, their development remains challenging because there are few datasets describing both the musculoskeletal geometry and muscle morphology from the elbow to the finger tips. Clinical imaging, optical motion capture and microscopy were used to create a dataset from a single specimen MSR-Action3D dataset is an action dataset of depth sequences captured by a depth camera. This dataset contains twenty actions: high arm wave, horizontal arm wave, hammer, hand catch, forward punch, high throw, draw x, draw tick, draw circle, hand clap, two hand wave, side-boxing, bend, forward kick, side kick, jogging, tennis swing, tennis serve, golf swing, pick up & throw From the results, we see clear benefits of using hand pose as a cue for action recognition compared to other data modalities. Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition

IPN Hand Dataset Papers With Cod

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MSRA Hand Dataset Papers With Cod

  1. Hardback. $176.00. eBook. $43.96. ISBN 9780367449667. Published June 30, 2020 by Chapman and Hall/CRC. 474 Pages. ISBN 9780412399206. Published November 1, 1993 by Chapman and Hall/CRC
  2. T hese pages describe a Hand Gesture dataset (HGds), a dataset composed of several annotated hand gestures captures performed by eleven different subjects and also, synthetically generated.. Some of the dictionaries included in this dataset can be found in the State Of Art: (Kollorz et al., 2008), (Molina et al., 2013) and (Soutschek et al., 2008
  3. Dynamic-hand-gesture is one of the representative biometric modalities, with advantages of safety and template-replaceability, has huge potential value. However, due to the lack of large-scale dataset and comprehensive evaluation methods, few researches are intended to study the dynamic-hand-gesture authentication method
  4. The files are named: grasping_dataset_<batch>.tfrecord-<k>-of-<n>. The number in the <batch> string is the number of features in each example. Batches that are larger than 500MB are split into parts. The script download_listing.sh linked at the bottom of the page will use curl to download all (886GB) of the data files
  5. Realtime and Robust Hand Tracking from Depth. Chen Qian, Xiao Sun, Yichen Wei, Xiaoou Tang and Jian Sun. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, Oral (dataset, demo video on Youtube

Two subjects were scanned, each in 12 separate hand poses, for a total of 24 MRI scans in the dataset. The 12 poses are the same for both subjects. By scanning the same subject in 12 different poses, one can infer how the internal hand anatomy (bones, muscles, tendons, etc.) moves and deforms as the hand articulates Figure 1: Depth and hand skeleton of the dataset. The hand skele-ton returned by the Intel RealSense camera contains 22 joints. The joints include: 1 for the center of the palm, 1 for the position of the wrist and 4 joints for each finger represent the tip, the 2 articula-tions and the base. All joints are represented in R3 The SecondHandSongs dataset is an independent dataset, but it only references songs that exist in the Million Song Dataset (MSD). The data mostly comes from the Second Hand Songs website. It was created as a collaboration between SecondHandSongs.com and the Million Song Dataset team

Challenge - Hands 201

  1. The dataset includes 7 active gestures (like hand flexion, extension, etc.) + idle and a set of trials with isometric contractions. sEMG was recorded using a 24-electrode matrix. How can I use the putEMG and putEMG-Force dataset? Follow the description in Download section, download manually or use automated scripts
  2. a hand model with 31 degrees of freedom (dof) with kine-matic constraints. The Big Hand data set contains 290,000 frames of egocentric hand poses, which is 130 times larger than the currently largest egocentric hand pose data set so far. Training a Convolutional Neural Network (CNN) on the data shows significantly improved results
  3. Download and Load the Used Cars Dataset. Since we will be using the used cars dataset, you will need to download this dataset. This dataset is already packaged and available for an easy download from the dataset page or directly from here Used Cars Dataset - usedcars.csv
  4. Hand, Chest X ray Datasets. I am working on archiving and retrieval the x ray images based on their type:like hand , chest e.t.c. from where i can get the dataset for this purpose. its a research based project.. Sign in to answer this question
  5. This post aims to introduce how to load MNIST (hand-written digit image) dataset using scikit-learn. Refernce. Scikit-learn Tutorial - introduction; Library¶ In [11]: from sklearn.datasets import load_digits import pandas as pd import matplotlib.pyplot as plt % matplotlib inlin

The Dynamic Hand gesture 14/28 dataset contains sequences of 14 hand gestures performed in two ways: using one finger and the whole hand. Each gesture is performed 5 times by 20 participants in 2 ways - described above - , resulting in 2800 sequences. All participants are right handed. Sequences are labelled following their gesture, the number of fingers used, the performer and the trial Rendered Hand Pose Dataset (RHD) is a synthetic RGB image based hand dataset, which is composed of 41258 images for training and 2728 images for testing with a resolution of , and it is obtained by requiring 20 different human models randomly to perform 39 different actions and randomly generate arbitrary backgrounds. The dataset is. Is there any other, openly available standard data set of English alphabets which can be used for testing the Handwritten Character Recognition system. image-processing dataset handwriting icr. Share. Improve this question. Follow edited Sep 2 '13 at 2:14. TheCodeArtist 3D hand shape and pose dataset as well as a small-scale real-world dataset, which contain the annotation of both 3D hand joint locations and the full 3D meshes of hand surface. We will share our datasets publicly upon the ac-ceptance of this work. We conduct comprehensive experiments on our proposed synthetic and real-world datasets as well as.