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Traditional work on bystander intervention in violent emergencies has found that the larger the group, the less the chance that any individual will intervene. Here, we tested the impact on helping behavior of the affiliation of the bystanders with respect to the participants. We recruited 40 male supporters of the U.K. Arsenal football club for a two-factor between groups study with 10 participants per group. Each participant spoke with a virtual human Arsenal supporter (V), the scenario displayed in a virtual reality system. During this conversation, another virtual character (P), not an Arsenal fan, verbally abused V for being an Arsenal fan leading eventually to physical pushing. There was a group of three virtual bystanders who were all either Arsenal supporters indicated by their shirts, or football fans wearing unbranded shirts. These bystanders either encouraged the participant to intervene or dissuaded him. We recorded the number of times that participants intervened to help V during the aggression. We found that participants were more likely to intervene when the bystanders were out-group with respect to the participant. By comparing levels of intervention with a “baseline” study (identical except for the presence of bystanders), we conclude that the presence of in-group bystanders decreases helping. We argue therefore that, other things being equal, diffusion of responsibility is more likely to be overcome when participant and victim share group membership, but bystanders do not. Our findings help to develop understanding of how diffusion of responsibility works by combining elements of both the bystander effect and the social identity approach to bystander behavior.

Abstract We present a novel approach for automatically detecting and tracking facial landmarks across poses and expressions from in-the-wild monocular video data, e.g., YouTube videos and smartphone recordings. Our method does not require any calibration or manual adjustment for new individual input videos or actors. Firstly, we propose a method of robust 2D facial landmark detection across poses, by combining shape-face canonical-correlation analysis with a global supervised descent method. Since 2D regression-based methods are sensitive to unstable initialization, and the temporal and spatial coherence of videos is ignored, we utilize a coarse-todense 3D facial expression reconstruction method to refine the 2D landmarks. On one side, we employ an in-the-wild method to extract the coarse reconstruction result and its corresponding texture using the detected sparse facial landmarks, followed by robust pose, expression, and identity estimation. On the other side, to obtain dense reconstruction results, we give a face tracking flow method that corrects coarse reconstruction results and tracks weakly textured areas; this is used to iteratively update the coarse face model. Finally, a dense reconstruction result is estimated after it converges. Extensive experiments on a variety of video sequences recorded by ourselves or downloaded from YouTube show the results of facial landmark detection and tracking under various lighting conditions, for various head poses and facial expressions. The overall performance and a comparison with state-of-art methods demonstrate the robustness and effectiveness of our method.

In order to reduce the data size and simplify the process of creating characters’ 3D models, a new and interactive ordinary differential equation (ODE)-based C2 continuous surface creation algorithm is introduced in this paper. With this approach, the creation of a three-dimensional surface is transformed into generating two boundary curves plus four control curves and solving a vector-valued sixth order ordinary differential equation subjected to boundary constraints consisting of boundary curves, and first and second partial derivatives at the boundary curves. Unlike the existing patch modeling approaches which require tedious and time-consuming manual operations to stitch two separate patches together to achieve continuity between two stitched patches, the proposed technique maintains the C2 continuity between adjacent surface patches naturally, which avoids manual stitching operations. Besides, compared with polygon surface modeling, our ODE C2 surface creation method can significantly reduce and compress the data size, deform the surface easily by simply changing the first and second partial derivatives, and shape control parameters instead of manipulating loads of polygon points.

Since non-automatic rigging requires heavy human involvements, and various automatic rigging algorithms are less efficient in terms of computational efficiency, especially for current curve-based skin deformation methods, identifying the iso-parametric curves and creating the animation skeleton requires tedious and time-consuming manual work. Although several automatic rigging methods have been developed, but they do not aim at curve-based models. To tackle this issue, this paper proposes a new rigging algorithm for automatic generation of dynamic skin deformation to quickly identify iso-parametric curves and create an animation skeleton in a few milliseconds, which can be seamlessly used in curve-based skin deformation methods to make the rigging process fast enough for highly efficient computer animation applications.