When responding to allegations of child sexual, physical, and psychological abuse, Child Protection Service (CPS) workers and police personnel need to elicit detailed and accurate accounts of the abuse to assist in decision-making and prosecution. Current research emphasizes the importance of the interviewer’s ability to follow empirically based guidelines. In doing so, it is essential to implement economical and scientific training courses for interviewers. Due to recent advances in artificial intelligence, we propose to generate a realistic and interactive child avatar, aiming to mimic a child. Our ongoing research involves the integration and interaction of different components with each other, including how to handle the language, auditory, emotional, and visual components of the avatar. This paper presents three subjective studies that investigate and compare various state-of-the-art methods for implementing multiple aspects of the child avatar. The first user study evaluates the whole system and shows that the system is well received by the expert and highlights the importance of its realism. The second user study investigates the emotional component and how it can be integrated with video and audio, and the third user study investigates realism in the auditory and visual components of the avatar created by different methods. The insights and feedback from these studies have contributed to the refined and improved architecture of the child avatar system which we present here.

Social media plays a significant role for sports organizations with millions of active fans, but publishing highlights is often a tedious manual operation. With the development of AI, new tools are available for content generation and personalization to engage audiences. We propose an AI-based multimedia production framework for the automatic publishing of soccer and ice hockey highlights on social media, and disseminate our experiences with developing dedicated pipelines for event detection and classification, player detection and tracking, highlight clipping, cropping, thumbnail generation, game summarization, caption generation, and social media sharing.

The rapid advancement of technology has been revolutionizing the field of sports media, where there is a growing need for sophisticated data processing methods. Current methodologies for extracting information from soccer broadcast videos to generate game highlights and summaries for social media are predominantly manual and rely heavily on text-based NLP techniques, overlooking the rich visual and auditory information available. In response to this challenge, our research introduces SoccerSum, a tool that innovates in the field by integrating computer vision, audio analysis with advanced language models like GPT-4. This multimodal approach enables automated, enriched content summarization, including detection of players and key field elements, thereby enhancing the metadata used in summarization algorithms. SoccerSum uniquely combines textual and visual data, offering a comprehensive solution for generating accurate, platform-specific content. This development represents a significant advancement in automated, data-driven sports media dissemination, and sets a new benchmark in the realm of soccer information extraction. A video of the demo can be found here: https://youtu.be/za4VIi2ARXY.

This paper presents a detailed study of an AI-driven platform designed for the training of child welfare and law enforcement professionals in conducting investigative interviews with maltreated children. It achieves a subjective simulation of interview situation through the integration of fine-tuned GPT-3 models within the Unity framework. The study recruited participants from a range of backgrounds, including professionals experienced in conducting investigative interviews and individuals with academic qualifications in psychology, criminology, or related disciplines. To assess the effectiveness of this tool, a multi-method evaluation approach was utilized, incorporating both quantitative analyses and qualitative interviews. The quantitative methods included mixed-effects models, which provided insights into how effects such as the type of virtual environment (2D vs. 3D), scenario variations, virtual reality (VR) familiarity, and professional expertise influence the user experience. Additionally, structural equation modeling (SEM) provided deeper insights into the relationships between variables, offering a comprehensive understanding of how they collectively impact the user experience. The qualitative method included a detailed semi-structured interview that provided a deeper understanding of user experiences and perceptions. The findings indicate significant advantages of the 3D environment in enhancing Flow and Virtual Fidelity ; however, the 2D environment was favored for Usability . Despite the 3D environment’s potential for greater immersion, the discomfort associated with VR head-mounted displays (HMDs) led some users to prefer the 2D setup. Familiarity with VR technology positively influenced user perception, indicating that prior exposure can mitigate some of the Avatar Interaction Comfort issues. Additionally, the Hand Movement Perception was better understood in scenarios with sensitive themes. As user experience increased, participants had a more positive view of the Age-Appropriate Response . Furthermore, the dialog systems effectiveness, particularly Response Relevance and Detailed Responses , played a significant role in Empathy Elicitation , often outweighing Virtual Fidelity . However, Emotion in facial expressions and Responsiveness were two factors that negatively impacted the effectiveness of the tool, indicating areas that need improvement in the future.

The current study aims to evaluate and compare the subjective quality of an AI-based training system developed for conducting child interviews, focusing on the distinction between immersive 3D (using virtual reality) and 2D desktop environments. To this end, a structured user study was conducted, involving 36 participants who were exposed to these two distinct environments. The study evaluated various aspects of user experience, namely presence, usability, visual fidelity, emotion, responsiveness, appropriateness, and training effectiveness. The findings reveal significant differences in user experience between the 2D and 3D environments. Notably, the 3D environment enhanced presence, visual fidelity, training effectiveness, and empathy. In contrast, the 2D environment was favored for usability. The study highlights the potential of immersive VR while also pointing out the need to improve the system response and emotional expressiveness of the avatars.

This paper introduces SoccerSum, a novel dataset aimed at enhancing object detection and segmentation in video frames depicting the soccer pitch, using footage from the Norwegian Eliteserien league across 2021-2023. With the goal of detecting elements beyond common entities in existing datasets, such as the soccer ball, players and referees, this dataset includes additional annotations for the goal net, corner flag posts, and the penalty mark. SoccerSum also includes the segmentation of key pitch areas such as the penalty and goal boxes for the same frame sequences. Comprising 750 frames annotated with 10 classes for advanced analysis, SoccerSum offers compatibility with existing frameworks, providing a rich dataset for the development of computer vision algorithms. This dataset not only serves as a resource for improving sports analytics, but also introduces a new application for automatic game summarization, enabling the generation of detailed and engaging content for fans and professionals. The SoccerSum dataset is accessible on Zenodo: https://zenodo.org/records/10612084.