Generative AI is increasingly adopted in sound practices, yet its use in professional production-oriented workflows remains insufficiently understood. This paper addresses this gap through an empirical study of game sound design, a structured creative practice, examining how game sound practitioners engage with generative AI tools and where current systems fail to support established workflows. We conducted a screening survey (n = 58) and semi-structured interviews involving hands-on use of two generative AI tools (n = 13) with professional game sound practitioners. Findings reveal that practitioners primarily use generative AI for early-stage ideation and rapid prototyping rather than production-ready outputs, and identify a structural misalignment between how current AI systems operate and how professional sound work is organized. We contribute a workflow model of professional game sound production, an analysis of audio teams’ organizational working conditions, and an initial framework for evaluating future AI-assisted game sound design tools grounded in practitioner interaction preferences.

This study investigated rear-seat passengers' motion discomfort when displaying (in)congruent visual motion while playing an immersive Augmented Reality (AR) racing game on a headrestmounted screen.29 players participated in two, 30-minute drives involving urban and highway roads.In the Synchronized Game (SG), the gameplay elements and video background were live-streamed from the vehicle's cameras and sensors, creating a congruent sensory environment.In the Desynchronized Game (DG), the gameplay was pre-recorded, resulting in incongruent visuo-vestibular motion where the visual motion in the game did not always align with that of the vehicle.Motion discomfort was measured at 2-min intervals using the MIsery SCale (MISC) and a thermal camera measuring participants' forehead temperature.The results showed that the SG condition led to significantly lower motion discomfort compared to the DG condition.These findings suggest that immersive games that incorporate real-time vehicle motion can help to mitigate motion discomfort by providing congruent visual-vestibular input. CCS Concepts• Human-centered computing → Mixed / augmented reality

Earthquakes significantly impact societies and economies, underscoring the need for effective search and rescue strategies. As AI and robotics increasingly support these efforts, the demand for high-fidelity, real-time simulation environments for training has become pressing. Earthquake simulation can be considered as a complex system. Traditional simulation methods, which primarily focus on computing intricate factors for single buildings or simplified architectural agglomerations, often fall short in providing realistic visuals and real-time structural damage assessments for urban environments. To address this deficiency, we introduce a real-time, high visual fidelity earthquake simulation platform based on the Chaos Physics System in Unreal Engine, specifically designed to simulate the damage to urban buildings. Initially, we use a genetic algorithm to calibrate material simulation parameters from Ansys into the Unreal Engine’s fracture system , based on real-world test standards. This alignment ensures the similarity of results between the two systems while achieving real-time capabilities. Additionally, by integrating real earthquake waveform data, we improve the simulation’s authenticity, ensuring it accurately reflects historical events. All functionalities are integrated into a visual user interface, enabling zero-code operation, which facilitates testing and further development by cross-disciplinary users. We verify the platform’s effectiveness through three AI-based tasks: similarity detection, path planning , and image segmentation. This paper builds upon the preliminary earthquake simulation study we presented at IMET 2023, with significant enhancements, including improvements to the material calibration workflow and the method for binding building foundations.

Despite the enhanced realism and immersion provided by VR headsets, users frequently encounter adverse effects such as digital eye strain (DES), dry eye, and potential long-term visual impairment due to excessive eye stimulation from VR displays and pressure from the mask. Recent VR headsets are increasingly equipped with eye-oriented monocular cameras to segment ocular feature maps. Yet, to compute the incident light stimulus and observe periocular condition alterations, it is imperative to transform these relative measurements into metric dimensions. To bridge this gap, we propose a lightweight framework derived from the U-Net 3 + deep learning backbone that we re-optimised, to estimate measurable periocular depth maps. Compatible with any VR headset equipped with an eye-oriented monocular camera, our method reconstructs three-dimensional periocular regions, providing a metric basis for related light stimulus calculation protocols and medical guidelines. Navigating the complexities of data collection, we introduce a Dynamic Periocular Data Generation (DPDG) environment based on UE MetaHuman, which synthesises thousands of training images from a small quantity of human facial scan data. Evaluated on a sample of 36 participants, our method exhibited notable efficacy in the periocular global precision evaluation experiment, and the pupil diameter measurement.