In scenographic productions, the selection of stage props is a structured process that follows a careful reading of the script and unfolds through three different phases: a visual exploration aimed at stimulating and shaping the creative vision, the selection of the props to be used on stage and their customization. While recent AI-driven approaches enable the automatic generation of 3D content, relatively few systems support sustained user involvement and iterative interpretive control throughout the authoring workflow. We present a WebXR-based Human-in-the-Loop system designed to support scenographic prop authoring by aligning AI assistance with this canonical workflow. The system provides AI-generated alternatives—images, retrieved 3D models, and textures—that users can iteratively explore, select, and refine while retaining control over creative decisions. We report an exploratory within-subject study with 20 experts in theater and cinema, comparing an AI-assisted workflow with a non-AI baseline. Results indicate that AI assistance was associated with higher perceived technology acceptance and creative support, alongside increased cognitive workload. Differences in the perceived coherence of the final props were not statistically significant, although descriptive trends suggest potential support for representing object type and symbolic meaning, indicating that AI assistance may be more effective in supporting early-stage exploration than precise semantic refinement.

Deep learning has proven to be one of the most effective methods in analyzing biological images to extract parameters fundamental for studying physiological functions and pathological conditions. In particular, when coupled with time-lapse microscopy (TLM), deep learning proves particularly effective in studying behaviors involving temporal dynamics. However, TLM videos are often affected by experimental noise and setup limitations, which can lead to inaccurate and poorly reproducible results. Taking advantage of the variational and generative capabilities of Variational Autoencoders (VAEs), we propose VAE-MOTION, a deep learning-based model for the analysis of cardiac contractile dynamics. By incorporating a temporal encoder into its architecture, our model allows the restoration of video quality by removing noise or increasing resolution, while simultaneously extracting accurate contraction-related signals from the latent space. The generation of synthetic videos allowed extensive training of VAE-MOTION, which subsequently validated on real videos from two different cardiac tissue models: 2D monolayers and 3D microtissues. VAE-MOTION was compared to two gold-standard methods in extracting contraction parameters relevant to drug efficacy or toxicity studies, demonstrating its potential for analyzing temporal dynamics in a given phenomenon or process.

Music in virtual environments has long been treated as a temporal evolving element, enhancing atmosphere and game pace but rarely considered as a performance adaptive element. Recent advances in artificial intelligence (AI) and procedural audio make it possible to generate music that adapts in real time to player actions and system state. Yet, despite its potential, the behavioural impact of such adaptive generative soundtracks in head-mounted display-based virtual reality (VR) remains largely unexplored. To address this gap, we introduce a VR archery system that integrates Google MusicFXDJ with Ubiq-Genie to deliver continuous AI-generated adaptive music driven by gameplay events. In a within-subjects experiment (N = 22), participants completed trials with either a stylistically-matched fixed soundtrack or an adaptive soundtrack that escalated tension across four phases as arrows depleted. Measures combined self-reported ratings of presence, focus, stress, and emotional impact, with performance metrics of accuracy and aiming time. Results reveal that adaptive generative music not only heightens immersion and emotional salience but also modulates motor precision in an arousal-dependent inverted-U pattern: moderate musical tension improved accuracy and speed, whereas excessive tension impaired them. These findings establish AI-generated music as a powerful behavioural feedback modality in VR, opening pathways for training, rehabilitation, and next-generation immersive entertainment.

Embodiment in mixed reality describes the sensation of experiencing a virtual representation as an extension of one’s own body. While research has extensively examined embodiment in virtual reality (VR) and head-mounted augmented reality (AR), its impact on smartphones remains underexplored. This study examines how smartphone-based AR embodiment affects user engagement and cognitive performance in a comprehension task. A study involving 24 participants explored whether using a smartphone AR face-filter to embody a virtual audience member influenced the recall of a historical speech. Findings show that participants in the AR condition scored higher on a factual quiz than those in the control group. At the same time, stronger perceived embodiment, especially self-location, was negatively associated with quiz performance, consistent with Cognitive Load Theory. These results should be interpreted cautiously: our comparison contrasted a static image (no AR) with AR that included facial embodiment, so we did not include an “AR without embodiment” condition to fully separate AR novelty from embodiment. Stimuli were also restricted to a single speech and a single historical scene presented as a static image, limiting generalizability to other content and to dynamic or interactive AR. Finally, the sample was modest (N=24), so estimates are preliminary and warrant replication. We discuss implications for designing smartphone AR that balances engagement with cognitive efficiency.

Conversational Virtual Agents (CVAs) offer a promising approach for enhancing user task performance in Mixed Reality (MR) environments. This paper explores the integration of a CVA into an MR application designed to assist in solving a 2D physical puzzle, offering enhanced spatial cognitive capabilities. Using the CVA classification architecture and the MiRAS (Mixed Reality Agents) Cube Taxonomy, we developed an MR system with a state-aware assistant to guide users in a puzzle-solving task only when requested. The primary research question is whether or not the CVA needs to be embodied. We conducted a study with 34 participants to investigate the influence of Voice-only and Embodied CVAs on puzzle-solving performance, user interactions with the assistant, the assistant’s social presence, overall system cognitive workload, and users’ perceptions of future system use. Both modalities showed equivalent outcomes regarding the number of position- and orientation-related queries, perceived usability, message and affective understanding, performance, frustration, and usefulness. However, results showed that Voice-only CVA significantly enhanced task efficiency: participants completed puzzles more quickly and accurately, reporting lower effort than in the Embodied condition. These findings suggest that Voice-only CVAs may be more effective for tasks like puzzle solving, where auditory guidance alone appears sufficient to support better performance.

External Ventricular Drain (EVD) placement is a complex neurosurgical task that requires identifying a target point within the brain and accurately positioning a catheter at the appropriate angle. While Mixed Reality (MR) technologies have seen limited adoption in the operating room, they offer significant potential for developing training systems that enhance skill acquisition and retention in unaided conditions. A current gap in research concerns the effectiveness of multimodal guidance systems that incorporate both visual and audio-based MR cues. In this paper, we present an MR-based simulator for EVD placement training and evaluate the impact of three MR-guided training modalities: (1) a baseline condition using only 2D CT scans and a 2D catheter projection; (2) a visual guidance modality incorporating a 3D trajectory overlay; and (3) an embodied-audio guidance modality featuring a virtual agent delivering spoken instructions and feedback. Participants underwent a digital training phase using one of the three modalities, followed by an unaided EVD placement on a physical phantom with a real catheter to evaluate skill transfer and retention. Results indicate that both advanced MR modalities significantly improve procedural accuracy, execution speed and receive higher scores in usability and technology acceptance compared to the baseline. Notably, training with 3D visual trajectory guidance led to significantly higher unaided placement accuracy, indicating stronger skill retention. However, multimodal guidance demonstrated equivalent execution speed, while showing a trend toward lower overall cognitive load.

Educational practices are increasingly experimenting with eXtended Reality (XR) paradigms to offer novel opportunities for boundaryless learning experiences with real-time interactions in immersive environments. Digital Twins (DT) are also gaining traction in this field to facilitate personalized learning experiences. However, a still unexplored space in learning frameworks amounts to the one where XR intersects with DTs. This work wants to move a step in such a direction with the design, implementation, and test of a DT-driven XR framework to learn procedural tasks. The framework offers three distinct learning modalities where virtual and physical interactions enhance learning retention by engaging users actively in digital and real-world environments. We contextualize such a framework for procedural task learning through one of its pivotal use cases: learning Rubik’s Cube notations. To evaluate and compare the effectiveness of these modalities, we perform an experimental user campaign evaluating short-term skill retention, performance accuracy, usability, and cognitive load of each of them. We then provide an extensive statistical analysis to compare each kind of guidance while analyzing correlations between the examined variables, offering insights into optimizing instructional methodologies within XR-based educational frameworks.

Regularization by Denoising (RED) is a well-known method for solving image restoration problems by using learned image denoisers as priors. Since the regularization parameter in the traditional RED does not have any physical interpretation, it does not provide an approach for automatic parameter selection. This letter addresses this issue by introducing the Constrained Regularization by Denoising (CRED) method that reformulates RED as a constrained optimization problem where the regularization parameter corresponds directly to the amount of noise in the measurements. The solution to the constrained problem is solved by designing an efficient method based on alternating direction method of multipliers (ADMM). Our experiments show that CRED outperforms the competing methods in terms of stability and robustness, while also achieving competitive performances in terms of image quality.

MOTIVATION: In fluorescence microscopy, single-molecule localization microscopy (SMLM) techniques aim at localizing with high-precision high-density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super resolution plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit. RESULTS: In this work, we propose a deep learning-based algorithm for precise molecule localization of high-density frames acquired by SMLM techniques whose ℓ2-based loss function is regularized by non-negative and ℓ0-based constraints. The ℓ0 is relaxed through its continuous exact ℓ0 (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data. AVAILABILITY AND IMPLEMENTATION: DeepCEL0 code is freely accessible at https://github.com/sedaboni/DeepCEL0.