Emiliano Gambaretto

Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation

An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mündermann et al. in Proceedings of CVPR, 2007) in terms of both accuracy and robustness. Since the method was originally developed for ≥8 cameras, the method performance was tested both (i) on the HumanEva II dataset (Sigal and Black, Technical Report CS-06-08, 2006) in a 4 camera configuration, (ii) on a series of motions including walking trials, a very challenging gymnastic motion and a dataset with motions similar to HumanEva II but with variable number of cameras.

Automatic Generation of a Subject-Specific Model for Accurate Markerless Motion Capture and Biomechanical Applications

A novel approach for the automatic generation of a subject-specific model consisting of morphological and joint location information is described. The aim is to address the need for efficient and accurate model generation for markerless motion capture (MMC) and biomechanical studies. The algorithm applied and expanded on previous work on human shapes space by embedding location information for ten joint centers in a subject-specific free-form surface. The optimal locations of joint centers in the 3-D mesh were learned through linear regression over a set of nine subjects whose joint centers were known. The model was shown to be sufficiently accurate for both kinematic (joint centers) and morphological (shape of the body) information to allow accurate tracking with MMC systems. The automatic model generation algorithm was applied to 3-D meshes of different quality and resolution such as laser scans and visual hulls . The complete method was tested using nine subjects of different gender, body mass index (BMI), age, and ethnicity. Experimental training error and cross-validation errors were 19 and 25 mm, respectively, on average over the joints of the ten subjects analyzed in the study.

Robotic alignment of femoral cutting mask during total knee arthroplasty

ObjectiveTo investigate a new navigation system integrated with a robotic arm for total knee replacement (TKR) procedures.Materials and MethodsThe study here reported attempts providing the surgeon with a robotic assistant handling the surgical tools with superior stability removing tremors. The system is equipped with an optical localization system, which allows the real-time monitoring of the position and orientation of the surgical tools carried by the robot end-effector and provides a feedback control to optimize the reaching of the goal position.ResultsPilot experiments, performed aligning the femoral cutting mask to the surgical position, together with the feasibility of the system, proved its accuracy and reliability.ConclusionThis paper shows the feasibility of a robotic system for TKR, integrated with a navigation system, in order to overcome limitations of both approaches.

Live real-time animation leveraging machine learning and game engine technology

classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. SIGGRAPH 2014, August 10 – 14, 2014, Vancouver, British Columbia, Canada. 2014 Copyright held by the Owner/Author. ACM 978-1-4503-2960-6/14/08 Live real-time animation leveraging machine learning and game engine technology