Building Information Modeling (BIM) has transformed the Architecture, Engineering, and Construction (AEC) industry by digitizing project data, yet its full potential remains unrealized due to persistent gaps between virtual models and physical sites. These gaps contribute to inefficiencies, with studies reporting substantial waste in labor and coordination. Extended Reality (XR) technologies offer a promising solution by enabling immersive, real-scale visualization of BIM models on-site. This article introduces a Mixed Reality (MR) application for Microsoft HoloLens 2 that superimposes BIM representations onto construction environments at a 1:1 scale, supporting real-time detection of differences between the as-designed BIM model and the as-built construction on site. We present a robust registration pipeline that integrates commercial XR hardware with advanced algorithms to achieve precise alignment under challenging conditions. To validate the system, we conducted a controlled user study comparing three registration paradigms (manual gesture-based, QR-assisted, and fully automatic) and analyzing their impact on alignment accuracy and user experience (UX) in AEC-related tasks. Results show that our automatic approach provides advantages over state-of-the-art alternatives and significantly improves registration precision and usability ratings over the baseline methods. Furthermore, the study demonstrates that alignment errors strongly influence spatial perception and decision-making, highlighting the necessity of high-fidelity registration for effective MR integration in construction workflows.

Abstract Creating realistic 3D human‐human interactions in virtual environments is challenging due to the high degrees of freedom in human body and the need for physically accurate poses that do not collide with each other. Traditional methods for humanhuman interaction are based on motion tracking or 3D body reconstruction, but lack generative capabilities. Recent generative methods enable the synthesis of individual or interacting motions via text or image input, but generally fall short in modeling close interactions. This paper introduces a novel generative model for close 3D human‐human interactions using a conditional variational autoencoder (cVAE), which generates poses for one human conditioned on the pose of another, allowing for controlled and diverse interaction synthesis. To train our model, we address two underlying long‐standing challenges in the field of human‐human interaction: data scarcity, for which we propose an automated supervised data augmentation strategy that generates synthetic yet realistic interaction poses; and collision awareness in generative approaches, for which we propose a self‐supervised loss based on a collision resolution technique using volumetric proxies to ensure physically correct interactions. We extensively evaluate the capabilities of our model, and demonstrate a wide variety of plausible and physically correct interactions, not possible to generate with current state‐of‐the‐art methods.

We propose a novel contact-aware method to synthesize highly-dense 3D crowds of animated characters. Existing methods animate crowds by, first, computing the 2D global motion approximating subjects as 2D particles and, then, introducing individual character motions without considering their surroundings. This creates the illusion of a 3D crowd, but, with density, characters frequently intersect each other since character-to-character contact is not modeled. We tackle this issue and propose a general method that considers any crowd animation and resolves existing residual collisions. To this end, we take a physics-based approach to model contacts between articulated characters. This enables the real-time synthesis of 3D high-density crowds with dozens of individuals that do not intersect each other, producing an unprecedented level of physical correctness in animations. Under the hood, we model each individual using a parametric human body incorporating a set of 3D proxies to approximate their volume. We then build a large system of articulated rigid bodies, and use an efficient physics-based approach to solve for individual body poses that do not collide with each other while maintaining the overall motion of the crowd. We first validate our approach objectively and quantitatively. We then explore relations between physical correctness and perceived realism based on an extensive user study that evaluates the relevance of solving contacts in dense crowds. Results demonstrate that our approach outperforms existing methods for crowd animation in terms of geometric accuracy and overall realism.