Xarnold: assessing the role of virtual humans in immersive fitness environments with aligned flat, multi-view, and 3D parametric visual instruction guidance

Recent advances in Extended Reality (XR) allow the creation of immersive fitness and virtual coaching systems. However, it remains unclear whether immersive visualization modalities influence users’ perception, understanding, and confidence in performing exercises. Moreover, it is unclear whether this improves those with respect to a classical 2D visualization. In this work, we investigate the role of a parametric 3D human model in enhancing workout perception and social presence within immersive fitness environments. To this date, we have designed a modular XR system, named XaRNold, that allows the visualization of single/multi-view video, and a parametric 3D model called Skinned Multi-Person Linear Model (SMPL) showing how to perform a physical exercise. We conducted a controlled user study (N=30) to compare participants’ cognitive load, understanding, technology acceptance, usability, and social perceptions and engagement across visualization modes. We employ the Fit3D dataset, which provides aligned 2D, multi-view, and related 3D poses data for diverse physical exercises. The results indicate that a parametric virtual human model provides a favorable trade-off between the measured constructs, offering a clearer understanding of why and how avatar-based guidance can be used and paving the way for more effective and empathic virtual fitness systems.