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      Panoptic Studio: A Massively Multiview System for Social Interaction Capture

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          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.

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          Most cited references 9

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          Rapid perceptual integration of facial expression and emotional body language.

          In our natural world, a face is usually encountered not as an isolated object but as an integrated part of a whole body. The face and the body both normally contribute in conveying the emotional state of the individual. Here we show that observers judging a facial expression are strongly influenced by emotional body language. Photographs of fearful and angry faces and bodies were used to create face-body compound images, with either matched or mismatched emotional expressions. When face and body convey conflicting emotional information, judgment of facial expression is hampered and becomes biased toward the emotion expressed by the body. Electrical brain activity was recorded from the scalp while subjects attended to the face and judged its emotional expression. An enhancement of the occipital P1 component as early as 115 ms after presentation onset points to the existence of a rapid neural mechanism sensitive to the degree of agreement between simultaneously presented facial and bodily emotional expressions, even when the latter are unattended.
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            Body cues, not facial expressions, discriminate between intense positive and negative emotions.

            The distinction between positive and negative emotions is fundamental in emotion models. Intriguingly, neurobiological work suggests shared mechanisms across positive and negative emotions. We tested whether similar overlap occurs in real-life facial expressions. During peak intensities of emotion, positive and negative situations were successfully discriminated from isolated bodies but not faces. Nevertheless, viewers perceived illusory positivity or negativity in the nondiagnostic faces when seen with bodies. To reveal the underlying mechanisms, we created compounds of intense negative faces combined with positive bodies, and vice versa. Perceived affect and mimicry of the faces shifted systematically as a function of their contextual body emotion. These findings challenge standard models of emotion expression and highlight the role of the body in expressing and perceiving emotions.
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              Image-based visual hulls

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                Author and article information

                Journal
                2016-12-09
                Article
                1612.03153

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence
                cs.CV

                Computer vision & Pattern recognition

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