Detecting cooperative partners in situations that have financial stakes is crucial to successful social exchange. The authors tested whether humans are sensitive to subtle facial dynamics of counterparts when deciding whether to trust and cooperate. Participants played a 2-person trust game before which the facial dynamics of the other player were manipulated using brief (<6 s) but highly realistic facial animations. Results showed that facial dynamics significantly influenced participants' (a) choice of with whom to play the game and (b) decisions to cooperate. It was also found that inferences about the other player's trustworthiness mediated these effects of facial dynamics on cooperative behavior.

BackgroundThe study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge.Main bodyThis narrative review considers the evolution of methods for extracting kinematic information from images, observing how technology has progressed from laborious manual approaches to optoelectronic marker-based systems. The motion analysis systems which are currently most widely used in sports biomechanics and rehabilitation do not allow kinematic data to be collected automatically without the attachment of markers, controlled conditions and/or extensive processing times. These limitations can obstruct the routine use of motion capture in normal training or rehabilitation environments, and there is a clear desire for the development of automatic markerless systems. Such technology is emerging, often driven by the needs of the entertainment industry, and utilising many of the latest trends in computer vision and machine learning. However, the accuracy and practicality of these systems has yet to be fully scrutinised, meaning such markerless systems are not currently in widespread use within biomechanics.ConclusionsThis review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics, while considering the challenges with accuracy and robustness that are yet to be addressed.

Shadows are ubiquitous in image and video data, and their removal is of interest in both Computer Vision and Graphics. We present an interactive, robust and high quality method for fast shadow removal. To perform detection we use an on-the-fly learning approach guided by two rough user inputs for the pixels of the shadow and the lit area. From this we derive a fusion image that magnifies shadow boundary intensity change due to illumination variation. After detection, we perform shadow removal by registering the penumbra to a normalised frame which allows us to efficiently estimate non-uniform shadow illumination changes, resulting in accurate and robust removal. We also present a reliable, validated and multi-scene category ground truth for shadow removal algorithms which overcomes issues such as inconsistencies between shadow and shadowfree images and limited variations in shadows. Using our data, we perform the most thorough comparison of state of the art shadow removal methods to date. Our algorithm outperforms the state of the art, and we supply our code and evaluation data and scripts to encourage future open comparisons. Shadow removal ground truth The first public data set was supplied in [2]. In our work, we propose a new data set that introduces multiple shadow categories, and overcomes potential environmental illumination and registration errors between the shadow and ground truth images. An example of comparison is shown in Fig. 1. Our new data set avoids these issues using a careful capture setup and a quantitative test for rejecting unavoidable capture failures due to environmental effects. Our images are also categorised according to 4 different attributes. . An example from our data without these properties is shown in (c).Our algorithm consists of 3 steps (see Fig. 2): 1) Pre-processing We detect an initial shadow mask ( Fig. 2(b)) using a KNN classifier trained from data from two rough user inputs (e.g. Fig. 2(a)). We generate a fusion image, which magnifies illumination discontinuities around shadow boundaries, by fusing channels of YCrCb colour space and suppressing texture (Fig. 2(c)).2) Penumbra unwrapping Based on the detected shadow mask and fusion image, we sample the pixel intensities of sampling lines perpendicular to the shadow boundary ( Fig. 2(d)), remove noisy ones and store the remaining as columns for the initial penumbra strip (Fig. 2(e)). We align the initial columns' illumination changes using its intensity conversion image ( Fig. 2(f)). This results in an aligned penumbra strip (Fig. 2(g)) whose conversion image (Fig. 2(h)) exhibits a stabler profile.3) Estimation of shadow scale and relighting Unlike previous work [1, 2], we do not assume a constrained model of illumination change. The columns of penumbra strip are first clustered into a few small groups. A unified sample can be synthesised by averaging the samples of each group (e.g. Fig. 2(i)). Our shadow scale is adaptively and quickly derived from the unified samples which cancel texture noise. The derived sparse scales f...

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