ABSTRACT
Chronic pain is a leading cause of morbidity among children and adolescents affecting 35% of the global population. Pediatric chronic pain management requires integrative health methods spanning physical and psychological subsystems through various mind-body interventions. Yoga therapy is one such method, known for its ability to improve the quality of life both physically and psychologically in chronic pain conditions. However, maintaining the clinical outcomes of personalized yoga therapy sessions at-home is challenging due to fear of movement, lack of motivation, and boredom. Virtual Reality (VR) has the potential to bridge the gap between the clinic and home by motivating engagement and mitigating pain-related anxiety or fear of movement. We developed a multi-modal algorithmic architecture for fusing real-time 3D human body pose estimation models with custom developed inverse kinematics models of physical movement to render biomechanically informed 6-DoF whole-body avatars capable of embodying an individual's real-time yoga poses within the VR environment. Experiments conducted among control participants demonstrated superior movement tracking accuracy over existing commercial off-the-shelf avatar tracking solutions, leading to successful embodiment and engagement. These findings demonstrate the feasibility of rendering virtual avatar movements that embody complex physical poses such as those encountered in yoga therapy. The impact of this work moves the field one step closer to an interactive system to facilitate at-home individual or group yoga therapy for children with chronic pain conditions.
ABSTRACT
We present an approach to capture the 3D motion of a group of people engaged in a social interaction. The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions. The Panoptic Studio is a system organized around the thesis that social interactions should be measured through the integration of perceptual analyses over a large variety of view points. We present a modularized system designed around this principle, consisting of integrated structural, hardware, and software innovations. The system takes, as input, 480 synchronized video streams of multiple people engaged in social activities, and produces, as output, the labeled time-varying 3D structure of anatomical landmarks on individuals in the space. Our algorithm is designed to fuse the "weak" perceptual processes in the large number of views by progressively generating skeletal proposals from low-level appearance cues, and a framework for temporal refinement is also presented by associating body parts to reconstructed dense 3D trajectory stream. Our system and method are the first in reconstructing full body motion of more than five people engaged in social interactions without using markers. We also empirically demonstrate the impact of the number of views in achieving this goal.
