One of the most relevant tasks for augmented and virtual reality applications is the interaction of virtual objects with real humans which requires accurate 3D human pose predictions. Obtaining accurate 3D human poses requires careful camera calibration which is difficult for nontechnical personal or in a pop-up scenario. Recent markerless motion capture approaches require accurate camera calibration at least for the final triangulation step. Instead, we solve this problem by presenting ElliPose, Stereoscopic 3D Human Pose Estimation by Fitting Ellipsoids, where we jointly estimate the 3D human as well as the camera pose. We exploit the fact that bones do not change in length over the course of a sequence and thus their relative trajectories have to lie on the surface of a sphere which we can utilize to iteratively correct the camera and 3D pose estimation. As another use-case we demonstrate that our approach can be used as replacement for ground-truth 3D poses to train monocular 3D pose estimators. We show that our method produces competitive results even when comparing with state-of-the-art methods that use more cameras or ground-truth camera extrinsics.

When humans have to solve everyday tasks, they simply pick the objects that are most suitable. While the question which object should one use for a specific task sounds trivial for humans, it is very difficult to answer for robots or other autonomous systems. This issue, however, is not addressed by current benchmarks for object detection that focus on detecting object categories. We therefore introduce the COCO-Tasks dataset which comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated. We furthermore propose an approach that detects the most suitable objects for a given task. The approach builds on a Gated Graph Neural Network to exploit the appearance of each object as well as the global context of all present objects in the scene. In our experiments, we show that the proposed approach outperforms other approaches that are evaluated on the dataset like classification or ranking approaches.

Traditionally, experimental economics uses controlled and incentivized field and lab experiments to analyze economic behavior. However, investigating peer effects in the classic settings is challenging due to the reflection problem: Who is influencing whom? To overcome this, we enlarge the methodological toolbox of these experiments by means of Virtual Reality. After introducing and validating a real-effort sorting task, we embed a virtual agent as peer of a human subject, who independently performs an identical sorting task. We conducted two experiments investigating (a) the subject's productivity adjustment due to peer effects and (b) the incentive effects on competition. Our results indicate a great potential for Virtual-Reality-based economic experiments.

The Passive A-Band Wind Sounder (PAWS) project is funded through NASA's Instrument Incubator Program (IIP). The objective of PAWS is to demonstrate an instrument concept for measuring wind speed profiles in the troposphere using Doppler shifts in selected oxygen absorption lines. PAWS is a daytime-only approach, but has the potential to provide better wind data than is currently available with significantly lower cost, risk, and platform requirements than lidar. This paper will provide an overview of the PAWS approach and progress on the instrument development.