## What is/are 3d Joint?

3d Joint - In order to study the 3D joint roughness coefficient (JRC), a series of direct shear tests were carried out and the surface morphologies of joints were tested using laser scanning technology.^{[1]}This is due to the similarities in 3D joint position space.

^{[2]}To address this issue, we propose a human structure-aware network, which is capable of recovering 3D joint locations from given 2D joint detections.

^{[3]}Additionally, we show that training with weak supervision in the form of 2D joint annotations on datasets of images in the wild, in conjunction with full supervision in the form of 3D joint annotations on limited available datasets allows for good generalization to 3D shape and pose predictions on images in the wild.

^{[4]}, bone proportions) together with 3D joint positions by enforcing the bone lengths consistency over a series of frames.

^{[5]}The first is the Convolutional Neural Network (CNN) based deep network which produces 3D joints positions from learned 3D bone vectors using a new layer.

^{[6]}For this we introduce an iterative refinement method that aligns the model-based 3D estimates of 2D/3D joint positions and DensePose with their image-based counterparts delivered by CNNs, achieving both model-based, global consistency and high spatial accuracy thanks to the bottom-up CNN processing.

^{[7]}Peak TFJ anterior shear force, peak axial TFJ compression force, and peak medial compartment TFJ compression force were estimated using a musculoskeletal model with inputs from 3D joint kinematics and inverse dynamics calculations.

^{[8]}In the developed system, a Microsoft Kinect sensor was used to capture 3D joint positions of the body of a trainer or trainee.

^{[9]}The accuracy and repeatability of the calibration procedure and the 3D joint angle estimation were validated against the gold standard motion capture system by an experimental study with ten able-bodied participants.

^{[10]}Numerical results show good performance of the 3D joint inversion method.

^{[11]}We consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations.

^{[12]}To use our method, we build a model, in which we design a particular SFR and its correlative DD which divided the 3D joint coordinates into two parts, plane coordinates and depth coordinates and use two modules named Plane Regression (PR) and Depth Regression (DR) to deal with them respectively.

^{[13]}To do this, we captured our own large-scale dataset composed of images of hands and the corresponding 3D joints annotations.

^{[14]}The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a colour encoding process.

^{[15]}Our system using 3D joint information obtained by using multiple Kinect v2 sensors and RNN-LSTM.

^{[16]}Background Our ultimate goal is to develop a valid human in vitro 3D joint model to simulate the pathogenesis of arthritis.

^{[17]}Once 3D joint poses are obtained, our framework estimates a plane containing the wrist and MCP joints and measures flexion/extension and abduction/aduction angles by applying computational geometry operations with respect to this plane.

^{[18]}This paper proposes a joint coordinate system for the analysis of sacroiliac joint motion, based on the procedure developed by Grood and Suntay, using semi‐automated anatomical landmarks on 3D joint surfaces.

^{[19]}Digital video was processed with a novel video-based assessment tool to produce 3D joint trajectories (PDAi), and joint angle and reach envelope measures were calculated from both data sources.

^{[20]}As shown by our evaluation, we achieve lower 3D joint error as well as better 2D overlay than the existing baselines.

^{[21]}2D joint points are firstly predicted by a CNN-based model called convolutional pose machine, and the 3D joint points are calculated using the depth image.

^{[22]}Most of the methods for predicting the 3D human pose from single picture are to first extract the 2d joint position in the image, and then use the 2d joint coordinates to get the 3d joint position.

^{[23]}Specifically, the VSA motion is described by its 3D joint position and its joint angles.

^{[24]}Therefore, this paper presents a deep network for recovering joint angles from 3D joint positions, which learns the prior dependence between them.

^{[25]}In a 3D joint, BS loading comprised of two equal forces applied at the two beam ends in the same direction while BCS loading comprised of two equal forces applied at the two beam ends in the opposite directions.

^{[26]}This chapter aims to provide such software to help reduce the risks of the operation by visualizing 3D joint anatomy of the specific patient for the surgeon, and letting surgeons observe the geometrical properties of the joint.

^{[27]}We demonstrate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly used 2D pose data.

^{[28]}We demonstrate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly used 2D pose data.

^{[29]}In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.

^{[30]}For this purpose, six half-scale 3D joint specimens were constructed and subjected to relevant tests.

^{[31]}Radial clearance, particularly the axial clearance in the 3D joint of a mechanism owing to the assemblage, manufacturing tolerances, wear, and other conditions, has become a research focus in the field of multibody dynamics in recent years.

^{[32]}In this paper, we propose a real-time framework that can not only estimate location of hands within a RGB image but also their corresponding 3D joint coordinates and their hand side determination of left or right handed simultaneously.

^{[33]}The aim of this study was to quantify 3D joint work at the hip, knee, and ankle during slope walking.

^{[34]}First, for each frame of a skeletal sequence, the histogram of 3D joints is weighted according to the contribution of joints in the corresponding class of human action.

^{[35]}The last part is to map the 2D skeleton sequence detected in the previous step into 3D space, the input is 2D joint point sequence, and the output is the corresponding 3D joint point sequence.

^{[36]}Based on these results a 3D joint shell model was generated and realized with a 3D printer.

^{[37]}In this paper, the 3D joint angles of the lower limbs are determined using both an IMU system and an optoelectronic system for twelve participants during stair ascent and descent, and inclined, declined and level walking.

^{[38]}1°) and RMSE of 3D joint angle estimation during over-ground walking.

^{[39]}A 3-D kinematic analysis was performed to measure 3D joint angles of the lower limb.

^{[40]}3D joints kinematics of the spine and lower limbs were compared between 20 healthy controls and 20 participants with non-specific LBP during walking, sit-to-stand and lifting.

^{[41]}3D joint kinematics can provide important information about the quality of movements.

^{[42]}And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part.

^{[43]}Moreover, given the scarcity of 3D hand-object manipulation benchmarks with joint annotations, we propose a new annotated synthetic dataset with realistic images, hand masks, joint masks and 3D joints coordinates.

^{[44]}With the 3D joints triangulated from multi-view 2D joints, a two-stage assembling method is proposed to select the correct 3D pose from thousands of pose seeds combined by joint semantic meanings.

^{[45]}Current approaches typically represent the skeleton of an articulate object as a set of 3D joints, which unfortunately ignores the relationship between joints, and fails to encode fine-grained anatomical constraints.

^{[46]}Development of 3D joints inference technology from 2D RGB video enables us to create 3D annotations for each frame of a video including movements.

^{[47]}In this paper, we propose a coarse-to-fine model to predict 3d joint locations progressively.

^{[48]}Key Points• A 3D joint CNN-RNN deep learning framework was developed for ICH detection and subtype classification, which has the flexibility to train with either subject-level labels or slice-level labels.

^{[49]}The second, more complex, solution is based on volumetric aggregation of 2D feature maps from the 2D backbone followed by refinement via 3D convolutions that produce final 3D joint heatmaps.

^{[50]}

## Corresponding 3d Joint

To do this, we captured our own large-scale dataset composed of images of hands and the corresponding 3D joints annotations.^{[1]}In this paper, we propose a real-time framework that can not only estimate location of hands within a RGB image but also their corresponding 3D joint coordinates and their hand side determination of left or right handed simultaneously.

^{[2]}The last part is to map the 2D skeleton sequence detected in the previous step into 3D space, the input is 2D joint point sequence, and the output is the corresponding 3D joint point sequence.

^{[3]}

## Produce 3d Joint

The first is the Convolutional Neural Network (CNN) based deep network which produces 3D joints positions from learned 3D bone vectors using a new layer.^{[1]}Digital video was processed with a novel video-based assessment tool to produce 3D joint trajectories (PDAi), and joint angle and reach envelope measures were calculated from both data sources.

^{[2]}

## Once 3d Joint

Once 3D joint poses are obtained, our framework estimates a plane containing the wrist and MCP joints and measures flexion/extension and abduction/aduction angles by applying computational geometry operations with respect to this plane.^{[1]}Once 3D joint poses are obtained, our framework estimates a plane containing the wrist and MCP joints and measures flexion/extension and abduction/adduction angles by applying computational geometry operations with respect to this plane.

^{[2]}

## 3d joint position

This is due to the similarities in 3D joint position space.^{[1]}, bone proportions) together with 3D joint positions by enforcing the bone lengths consistency over a series of frames.

^{[2]}For this we introduce an iterative refinement method that aligns the model-based 3D estimates of 2D/3D joint positions and DensePose with their image-based counterparts delivered by CNNs, achieving both model-based, global consistency and high spatial accuracy thanks to the bottom-up CNN processing.

^{[3]}In the developed system, a Microsoft Kinect sensor was used to capture 3D joint positions of the body of a trainer or trainee.

^{[4]}Most of the methods for predicting the 3D human pose from single picture are to first extract the 2d joint position in the image, and then use the 2d joint coordinates to get the 3d joint position.

^{[5]}Specifically, the VSA motion is described by its 3D joint position and its joint angles.

^{[6]}Therefore, this paper presents a deep network for recovering joint angles from 3D joint positions, which learns the prior dependence between them.

^{[7]}In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.

^{[8]}

## 3d joint angle

The accuracy and repeatability of the calibration procedure and the 3D joint angle estimation were validated against the gold standard motion capture system by an experimental study with ten able-bodied participants.^{[1]}We demonstrate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly used 2D pose data.

^{[2]}We demonstrate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly used 2D pose data.

^{[3]}In this paper, the 3D joint angles of the lower limbs are determined using both an IMU system and an optoelectronic system for twelve participants during stair ascent and descent, and inclined, declined and level walking.

^{[4]}1°) and RMSE of 3D joint angle estimation during over-ground walking.

^{[5]}A 3-D kinematic analysis was performed to measure 3D joint angles of the lower limb.

^{[6]}Due to the independence of the proposed method from the magnetic condition, the proposed approach could be reliably applied in various fields that require robust 3D joint angle estimation through IMU signals in an unspecified arbitrary magnetic environment.

^{[7]}In particular, the mesh representation is achieved by parameterizing a generic 3D hand model with shape and relative 3D joint angles.

^{[8]}

## 3d joint location

To address this issue, we propose a human structure-aware network, which is capable of recovering 3D joint locations from given 2D joint detections.^{[1]}We consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations.

^{[2]}In this paper, we propose a coarse-to-fine model to predict 3d joint locations progressively.

^{[3]}(2) A multi-task learning (MTL) approach to predicting multiple outputs such as shape, 3D joint locations, pose angles, and body volume.

^{[4]}

## 3d joint coordinate

To use our method, we build a model, in which we design a particular SFR and its correlative DD which divided the 3D joint coordinates into two parts, plane coordinates and depth coordinates and use two modules named Plane Regression (PR) and Depth Regression (DR) to deal with them respectively.^{[1]}The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a colour encoding process.

^{[2]}In this paper, we propose a real-time framework that can not only estimate location of hands within a RGB image but also their corresponding 3D joint coordinates and their hand side determination of left or right handed simultaneously.

^{[3]}

## 3d joint point

2D joint points are firstly predicted by a CNN-based model called convolutional pose machine, and the 3D joint points are calculated using the depth image.^{[1]}The last part is to map the 2D skeleton sequence detected in the previous step into 3D space, the input is 2D joint point sequence, and the output is the corresponding 3D joint point sequence.

^{[2]}we use convolutional neural networks for 2D human pose estimation to get joint points coordinates in color image and then map the returned results to corresponding depth image to obtain 3D joint points information.

^{[3]}

## 3d joint kinematic

Peak TFJ anterior shear force, peak axial TFJ compression force, and peak medial compartment TFJ compression force were estimated using a musculoskeletal model with inputs from 3D joint kinematics and inverse dynamics calculations.^{[1]}3D joint kinematics can provide important information about the quality of movements.

^{[2]}

## 3d joint pose

Once 3D joint poses are obtained, our framework estimates a plane containing the wrist and MCP joints and measures flexion/extension and abduction/aduction angles by applying computational geometry operations with respect to this plane.^{[1]}Once 3D joint poses are obtained, our framework estimates a plane containing the wrist and MCP joints and measures flexion/extension and abduction/adduction angles by applying computational geometry operations with respect to this plane.

^{[2]}

## 3d joint error

As shown by our evaluation, we achieve lower 3D joint error as well as better 2D overlay than the existing baselines.^{[1]}We show quantitatively that introducing scene constraints significantly reduces 3D joint error and vertex error.

^{[2]}