Consumer 3D scanners and depth cameras are increasingly being used to generate content and avatars for Virtual Reality (VR) environments and avoid the inconveniences of hand modeling; however, it is sometimes difficult to evaluate quantitatively the mesh quality at which consumer available 3D scans should be exported, and whether the object perception might be affected by its shading. We propose using a paired-comparisons test based on psychophysics of perception to do that evaluation. As psychophysics is not subject to opinion, skill level, mental state, or economic situation it can be considered a quantitative way to measure how people perceive the mesh quality. In particular, we compare four different levels of mesh quality (1K, 5K, 10K and 20K triangles). We present two studies within subjects: in one we investigate the influences of seeing an object in a regular screen vs. in a Head Mounted Display (HMD); while in the second experiment we aim at detecting the effects of shading into quality perception. At each iteration of the pair-test comparisons participants pick the mesh that they think had higher quality; by the end of the experiment we compile a preference matrix. The results show a correlation between real and assessed quality, despite participants' reported uncertainty. We also find an interaction with quality and shading, which gains importance for quality perception when the mesh has high definition. Furthermore, we assess the subjective realism of the most/least preferred scans using an Immersive Augmented Reality (IAR) video-see-through setup to compare the real vs the 3D scanned object in the same HMD environment. Results show higher levels of realism were perceived through the HMD than when using a regular monitor, although the quality was similarly perceived in both systems.

We describe a system that allows for controlling different robots and avatars from a real time motion stream. The underlying problem is that motion data from tracking systems is usually represented differently to the motion data required to drive an avatar or a robot: there may be different joints, motion may be represented by absolute joint positions and rotations or by a root position, bone lengths and relative rotations in the skeletal hierarchy. Our system resolves these issues by remapping in real time the tracked motion so that the avatar or robot performs motions that are visually close to those of the tracked person. The mapping can also be reconfigured interactively at run-time. We demonstrate the effectiveness of our system by case studies in which a tracked person is embodied as an avatar in immersive virtual reality or as a robot in a remote location. We show this with a variety of tracking systems, humanoid avatars and robots.