Abstract Human shape spaces have been extensively studied, as they are a core element of human shape and pose inference tasks. Classic methods for creating a human shape model register a surface template mesh to a database of 3D scans and use dimensionality reduction techniques, such as Principal Component Analysis, to learn a compact representation. While these shape models enable global shape modifications by correlating anthropometric measurements with the learned subspace, they only provide limitedlocalizedshape control. We instead register a volumetric anatomical template, consisting of skeleton bones and soft tissue, to the surface scans of the CAESAR database. We further enlarge our training data to the full Cartesian product of all skeletons and all soft tissues using physically plausible volumetric deformation transfer. This data is then used to learn an anatomically constrained volumetric human shape model in a self‐supervised fashion. The resultingTailorMemodel enables shape sampling, localized shape manipulation, and fast inference from given surface scans.

This article investigates the effects of different XR displays on the perception and plausibility of personalized virtual humans. We compared immersive virtual reality (VR), video see-through augmented reality (VST AR), and optical see-through AR (OST AR). The personalized virtual alter egos were generated by state-of-the-art photogrammetry methods. 42 participants were repeatedly exposed to animated versions of their 3D-reconstructed virtual alter egos in each of the three XR display conditions. The reconstructed virtual alter egos were additionally modified in body weight for each repetition. We show that the display types lead to different degrees of incongruence between the renderings of the virtual humans and the presentation of the respective environmental backgrounds, leading to significant effects of perceived mismatches as part of a plausibility measurement. The device-related effects were further partly confirmed by subjective misestimations of the modified body weight and the measured spatial presence. Here, the exceedingly incongruent OST AR condition leads to the significantly highest weight misestimations as well as to the lowest perceived spatial presence. However, similar effects could not be confirmed for the affective appraisal (i.e., humanness, eeriness, or attractiveness) of the virtual humans, giving rise to the assumption that these factors might be unrelated to each other.

This paper introduces an automated 3D-reconstruction method for generating high-quality virtual humans from monocular smartphone cameras. The input of our approach are two video clips, one capturing the whole body and the other providing detailed close-ups of head and face. Optical flow analysis and sharpness estimation select individual frames, from which two dense point clouds for the body and head are computed using multi-view reconstruction. Automatically detected landmarks guide the fitting of a virtual human body template to these point clouds, thereby reconstructing the geometry. A graph-cut stitching approach reconstructs a detailed texture. Our results are compared to existing low-cost monocular approaches as well as to expensive multi-camera scan rigs. We achieve visually convincing reconstructions that are almost on par with complex camera rigs while surpassing similar low-cost approaches. The generated high-quality avatars are ready to be processed, animated, and rendered by standard XR simulation and game engines such as Unreal or Unity.