With escalating environmental damage to ecosystems, plants, and wildlife, raising environmental awareness has become not just an academic focus but a necessity. New paradigms are needed to foster this awareness, and Timothy Morton’s concept of hyperobjects is one such approach, forming the basis of this paper. Our literature review reveals that this paradigm has rarely been applied in VR applications. We present a virtual reality (VR) experience, Flourish, inspired by hyperobject principles such as interconnectedness, temporal depth, and a non-human perspective, to test their effectiveness in changing users’ self-efficacy regarding climate change and ecosystem preservation. We design the experience using the BehaveFIT framework to address potential psychological barriers. Our results show that Flourish had a short-term positive impact and holds promise as a tool for promoting environmental awareness. We thus believe that the use of hyperobject paradigms with VR can be fruitful and offer options for future, more integrated work.

As deep learning systems increasingly depend on human image and video data, privacy concerns make real data collection costly and time-consuming. Many companies turn to synthetic data, which is easier to acquire, GDPR-compliant, and helps reduce model bias. However, creating diverse human poses remains labor-intensive. To address this, we introduce VRPoser, a VR-based serious game and tool for intuitive pose creation. Users take on the role of artists, completing story-driven quests to pose mannequins. These poses are converted to OpenPose skeletons and used with ControlNet and Stable Diffusion to generate synthetic images. When evaluated for usability, VRPoser was found to be intuitive, fun, and efficient. Comparisons with standard posing software show that VRPoser achieves similar results in less time.

Supporting museum engagement and learning opportunities for a younger audience requires the adaptation of modern technologies and more diverse ways of communicating information and stories. This study explores the integration of Large Language Models (LLMs) as non-playable character (NPC) guides into Virtual Reality (VR) to enhance social presence and learning engagement in historical contexts. Collaborating with the Lindholm Høje Viking Museum in Denmark, we developed a VR experience featuring context-aware LLM-integrated digital social actors portraying prehistoric Vikings. Three interactive experiences are designed around the exhibitions present in the museum. Through a comparison with a traditional finite-state dialogue version, our findings suggest an overall increase in user presence and engagement when interacting with LLM-integrated NPCs.

Combining modern technologies like large-language models (LLMs), speech-to-text, and text-to-speech can enhance immersion in virtual reality (VR) environments. However, challenges exist in effectively implementing LLMs and educating users. This paper explores implementing LLM-powered virtual social actors and facilitating user communication. We developed a murder mystery game where users interact with LLM-based non-playable characters (NPCs) through interrogation, clue-gathering, and exploration. Two versions were tested: one using speech recognition and another with traditional dialog boxes. While both provided similar social presence, users felt more immersed with speech recognition but found it overwhelming, while the dialog version was more challenging. Slow NPC response times were a source of frustration, highlighting the need for faster generation or better masking for a seamless experience.

One of the time consuming tasks in the timber industry is the manually measurement of features of wood stacks. Such features include, but are not limited to, the number of the logs in a stack, their diameters distribution, and their volumes. Computer vision techniques have recently been used for solving this real-world industrial application. Such techniques are facing many challenges as the task is usually performed in outdoor, uncontrolled, environments. Furthermore, the logs can vary in texture and they can be occluded by different obstacles. These all make the segmentation of the wood logs a difficult task. Graph-cut has shown to be good enough for such a segmentation. However, it is hard to find proper graph weights. This is exactly the contribution of this paper to propose a method for setting the weights of the graph. To do so, we use Circular Hough Transform (CHT) for obtaining information about the fore and background regions of a stack image, and then use this together with a Local Circularity Measure (LCM) to modify the weights of the graph to segment the wood logs from the rest of the image. We further improve the segmentation by separating overlapping logs. These segmented wood logs are finally scaled and used to acquire the necessary wood stack measurements in real-world scale (in cm). The proposed system, which works automatically, has been tested on two different datasets, containing real outdoor images of logs which vary in shapes and sizes. The experimental results show that the proposed approach not only achieves the same results as the state-of-the-art systems, it produces more stable results.