A survey on human performance capture and animation

With the rapid development of computing technology, three-dimensional (3D) human body models and their dynamic motions are widely used in the digital entertainment industry. Human perfor- mance mainly involves human body shapes and motions. Key research problems include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical e�ects. In this survey, according to main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion sim- ulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animatio

Collaborative Surgical Robots:Optical Tracking During Endovascular Operations

Endovascular interventions usually require meticulous handling of surgical instruments and constant monitoring of the operating room workspace. To address these challenges, robotic- assisted technologies and tracking techniques are increasingly being developed. Specifically, the limited workspace and potential for a collision between the robot and surrounding dynamic obstacles are important aspects that need to be considered. This article presents a navigation system developed to assist clinicians with the magnetic actuation of endovascular catheters using multiple surgical robots. We demonstrate the actuation of a magnetic catheter in an experimental arterial testbed with dynamic obstacles. The motions and trajectory planning of two six degrees of freedom (6-DoF) robotic arms are established through passive markerguided motion planning. We achieve an overall 3D tracking accuracy of 2.3 ± 0.6 mm for experiments involving dynamic obstacles. We conclude that integrating multiple optical trackers with the online planning of two serial-link manipulators is useful to support the treatment of endovascular diseases and aid clinicians during interventions

GRAB: A Dataset of Whole-Body Human Grasping of Objects

Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While "grasping" is commonly thought of as a single hand stably lifting an object, we capture the motion of the entire body and adopt the generalized notion of "whole-body grasps". Thus, we collect a new dataset, called GRAB (GRasping Actions with Bodies), of whole-body grasps, containing full 3D shape and pose sequences of 10 subjects interacting with 51 everyday objects of varying shape and size. Given MoCap markers, we fit the full 3D body shape and pose, including the articulated face and hands, as well as the 3D object pose. This gives detailed 3D meshes over time, from which we compute contact between the body and object. This is a unique dataset, that goes well beyond existing ones for modeling and understanding how humans grasp and manipulate objects, how their full body is involved, and how interaction varies with the task. We illustrate the practical value of GRAB with an example application; we train GrabNet, a conditional generative network, to predict 3D hand grasps for unseen 3D object shapes. The dataset and code are available for research purposes at https://grab.is.tue.mpg.de.Comment: ECCV 202

Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at http://github.com/mohomran/neural_body_fittingComment: 3DV 201

Application and Evaluation of Lighthouse Technology for Precision Motion Capture

This thesis presents the development towards a system that can capture and quantify motion for applications in biomechanical and medical fields demanding precision motion tracking using the lighthouse technology. Commercially known as SteamVR tracking, the lighthouse technology is a motion tracking system developed for virtual reality applications that makes use of patterned infrared light sources to highlight trackers (objects embedded with photodiodes) to obtain their pose or spatial position and orientation. Current motion capture systems such as the camera-based motion capture are expensive and not readily available outside of research labs. This thesis provides a case for low-cost motion capture systems. The technology is applied to quantify motion to draw inferences about biomechanics capture and analysis, quantification of gait, and prosthetic alignment. Possible shortcomings for data acquisition using this system for the stated applications have been addressed. The repeatability of the system has been established by determining the standard deviation error for multiple trials based on a motion trajectory using a seven degree-of-freedom robot arm. The accuracy testing for the system is based on cross-validation between the lighthouse technology data and transformations derived using joint angles by developing a forward kinematics model for the robot’s end-effector pose. The underlying principle for motion capture using this system is that multiple trackers placed on limb segments allow to record the position and orientation of the segments in relation to a set global frame. Joint angles between the segments can then be calculated from the recorded positions and orientations of each tracker using inverse kinematics. In this work, inverse kinematics for rigid bodies was based on calculating homogeneous transforms to the individual trackers in the model’s reference frame to find the respective Euler angles as well as using the analytical approach to solve for joint variables in terms of known geometric parameters. This work was carried out on a phantom prosthetic limb. A custom application-specific motion tracker was also developed using a hardware development kit which would be further optimized for subsequent studies involving biomechanics motion capture

Articulation estimation and real-time tracking of human hand motions

Schröder M. Articulation estimation and real-time tracking of human hand motions. Bielefeld: Universität Bielefeld; 2015.This thesis deals with the problem of estimating and tracking the full articulation of human hands. Algorithmically recovering hand articulations is a challenging problem due to the hand’s high number of degrees of freedom and the complexity of its motions. Besides the accuracy and efficiency of the hand posture estimation, hand tracking methods are faced with issues such as invasiveness, ease of deployment and sensor artifacts. In this thesis several different hand tracking approaches are examined, including marker-based optical motion capture, data-driven discriminative visual tracking and generative tracking based on articulated registration, and various contributions to these areas are presented. The problem of optimally placing reduced marker sets on a performer’s hand for optical hand motion capture is explored. A method is proposed that automatically generates functional reduced marker layouts by optimizing for their numerical stability and geometric feasibility. A data-driven discriminative tracking approach based on matching the hand’s appearance in the sensor data with an image database is investigated. In addition to an efficient nearest neighbor search for images, a combination of discriminative initialization and generative refinement is employed. The method’s applicability is demonstrated in interactive robot teleoperation. Various real human hand motions are captured and statistically analyzed to derive low-dimensional representations of hand articulations. An adaptive hand posture subspace concept is developed and integrated into a generative real-time hand tracking approach that aligns a virtual hand model with sensor point clouds based on constrained inverse kinematics. Generative hand tracking is formulated as a regularized articulated registration process, in which geometrical model fitting is combined with statistical, kinematic and temporal regularization priors. A registration concept that combines 2D and 3D alignment and explicitly accounts for occlusions and visibility constraints is devised. High-quality, non-invasive, real-time hand tracking is achieved based on this regularized articulated registration formulation