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P.S. Cesar Garcia
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Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article. Through co-design sessions (N=10), we elicited 69 disclosure designs and implemented four prototypes that visually disclose human-AI collaboration in journalism. We then ran a within-subjects lab study (N=32) to examine how disclosure visualizations (Textual, Role-based Timeline, Task-based Timeline, Chatbot) and collaboration ratios (Primarily Human vs. Primarily AI) influenced visualization perceptions, gaze patterns, and post-experience responses. We found that textual disclosures were least effective in communicating human-AI collaboration, whereas Chatbot offered the most in-depth information. Furthermore, while role-based timelines amplified AI contribution in primarily human articles, task-based timeline shifted perceptions toward human involvement in primarily AI articles. We contribute Human-AI collaboration disclosure visualizations and their evaluation, and cautionary considerations on how visualizations can alter perceptions of AI's actual role during news article creation.
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Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article. Through co-design sessions (N=10), we elicited 69 disclosure designs and implemented four prototypes that visually disclose human-AI collaboration in journalism. We then ran a within-subjects lab study (N=32) to examine how disclosure visualizations (Textual, Role-based Timeline, Task-based Timeline, Chatbot) and collaboration ratios (Primarily Human vs. Primarily AI) influenced visualization perceptions, gaze patterns, and post-experience responses. We found that textual disclosures were least effective in communicating human-AI collaboration, whereas Chatbot offered the most in-depth information. Furthermore, while role-based timelines amplified AI contribution in primarily human articles, task-based timeline shifted perceptions toward human involvement in primarily AI articles. We contribute Human-AI collaboration disclosure visualizations and their evaluation, and cautionary considerations on how visualizations can alter perceptions of AI's actual role during news article creation.
Social XR for Pre-Production Meetings
An In-the-Wild Study of Early Stage Communication between XR Producers and Clients
Conference paper
(2026)
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Sueyoon Lee, Irene Viola, Jack Jansen, Ashutosh Singla, Karolina Wylezek, Thomas Röggla, Pablo Cesar
Professional collaboration in XR production requires aligning spatial vision between stakeholders with different roles and expertise. While producers understand XR design affordances, clients often lack experiential knowledge, creating communication gaps during early project discussions. Conventional 2D meeting platforms cannot adequately support discussing inherently spatial XR concepts. We explore how Social XR addresses this challenge by situating both parties within a shared three-dimensional meeting environment. Through an in-the-wild study with XR producers in Denmark and museum curators in the Netherlands (N=8), we examine how shared immersive space shapes pre-production communication. Our findings demonstrate Social XR's value for improving communication compared to conventional meetings: clients gain direct spatial understanding of XR possibilities, while producers can observe client reactions and guide discussions in real time. The study also reveals divergent meeting intentions between producers and clients - a dynamic invisible in 2D contexts. Additionally, we discuss implications for designing and evaluating collaborative XR meeting environments where success depends on aligning creative vision rather than completing defined tasks.
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Professional collaboration in XR production requires aligning spatial vision between stakeholders with different roles and expertise. While producers understand XR design affordances, clients often lack experiential knowledge, creating communication gaps during early project discussions. Conventional 2D meeting platforms cannot adequately support discussing inherently spatial XR concepts. We explore how Social XR addresses this challenge by situating both parties within a shared three-dimensional meeting environment. Through an in-the-wild study with XR producers in Denmark and museum curators in the Netherlands (N=8), we examine how shared immersive space shapes pre-production communication. Our findings demonstrate Social XR's value for improving communication compared to conventional meetings: clients gain direct spatial understanding of XR possibilities, while producers can observe client reactions and guide discussions in real time. The study also reveals divergent meeting intentions between producers and clients - a dynamic invisible in 2D contexts. Additionally, we discuss implications for designing and evaluating collaborative XR meeting environments where success depends on aligning creative vision rather than completing defined tasks.
Conference paper
(2026)
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Karolina Wylezek, Irene Viola, Silvia Rossi, Jack Jansen, Thomas Röggla, Pablo Cesar
Social museums constantly search for new ways to satisfy and educate their visitors. One of the most important elements to success in those objectives is good exhibition design. It can be achieved by incorporating context, which is proven to improve understanding of exhibits and knowledge retention, and enhance the overall experience of the museum visit. While context is known to shape visitor experience in physical museums, its role in virtual reality museums remains underexplored, despite the flexibility in environment manipulation allowing for further adjustments. Moreover, social VR experiences allow for social interactions, which are crucial for the visitors' satisfaction and further improve their learning outcomes. In this study, we design and implement a social VR fashion exhibition, which we evaluate in a real museum setting during a three-day event attended primarily by cultural heritage professionals. We also conduct a between-subject user study (N=56) with a varied group of end-users to explore the influence of context on users' learning, experience, and sociality in social VR exhibitions. To do that, we design two exhibition rooms with identical exhibits and information: one providing historical context through the surrounding environment (objects and style) without adding extra explicit knowledge, and one neutral without the contextually adjusted environment. During experiments, we collect quantitative data from questionnaire results, behavioral data, and qualitative insights through semi-structured interviews. The results show that when the context fits the exhibits, the participants' learning and experience improve. These results bring knowledge for future social VR exhibition designers on how to approach environment design in their projects.
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Social museums constantly search for new ways to satisfy and educate their visitors. One of the most important elements to success in those objectives is good exhibition design. It can be achieved by incorporating context, which is proven to improve understanding of exhibits and knowledge retention, and enhance the overall experience of the museum visit. While context is known to shape visitor experience in physical museums, its role in virtual reality museums remains underexplored, despite the flexibility in environment manipulation allowing for further adjustments. Moreover, social VR experiences allow for social interactions, which are crucial for the visitors' satisfaction and further improve their learning outcomes. In this study, we design and implement a social VR fashion exhibition, which we evaluate in a real museum setting during a three-day event attended primarily by cultural heritage professionals. We also conduct a between-subject user study (N=56) with a varied group of end-users to explore the influence of context on users' learning, experience, and sociality in social VR exhibitions. To do that, we design two exhibition rooms with identical exhibits and information: one providing historical context through the surrounding environment (objects and style) without adding extra explicit knowledge, and one neutral without the contextually adjusted environment. During experiments, we collect quantitative data from questionnaire results, behavioral data, and qualitative insights through semi-structured interviews. The results show that when the context fits the exhibits, the participants' learning and experience improve. These results bring knowledge for future social VR exhibition designers on how to approach environment design in their projects.
Conference paper
(2026)
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Pooja Prajod, Hannes Cools, Thomas Röggla, Karthikeya Puttur Venkatraj, Amber Kusters, Alia Elkattan, Pablo Cesar, Abdallah El Ali
As artificial intelligence (AI) is increasingly integrated into news production, calls for transparency about the use of AI have gained considerable traction. Recent studies suggest that AI disclosures can lead to a "transparency dilemma", where disclosure reduces readers' trust. However, little is known about how the level of detail in AI disclosures influences trust and contributes to this dilemma within the news context. In this 3×2×2 mixed factorial study with 40 participants, we investigate how three levels of AI disclosures (none, one-line, detailed) across two types of news (politics and lifestyle) and two levels of AI involvement (low and high) affect news readers' trust. We measured trust using the News Media Trust questionnaire, along with two decision behaviors: source-checking and subscription decisions. Questionnaire responses and subscription rates showed a decline in trust only for detailed AI disclosures, whereas source-checking behavior increased for both one-line and detailed disclosures, with the effect being more pronounced for detailed disclosures. Insights from semi-structured interviews suggest that source-checking behavior was primarily driven by interest in the topic, followed by trust, whereas trust was the main factor influencing subscription decisions. Around two-thirds of participants expressed a preference for detailed disclosures, while most participants who preferred one-line indicated a need for detail-on-demand disclosure formats. Our findings show that not all AI disclosures lead to a transparency dilemma, but instead reflect a trade-off between readers' desire for more transparency and their trust in AI-assisted news content.
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As artificial intelligence (AI) is increasingly integrated into news production, calls for transparency about the use of AI have gained considerable traction. Recent studies suggest that AI disclosures can lead to a "transparency dilemma", where disclosure reduces readers' trust. However, little is known about how the level of detail in AI disclosures influences trust and contributes to this dilemma within the news context. In this 3×2×2 mixed factorial study with 40 participants, we investigate how three levels of AI disclosures (none, one-line, detailed) across two types of news (politics and lifestyle) and two levels of AI involvement (low and high) affect news readers' trust. We measured trust using the News Media Trust questionnaire, along with two decision behaviors: source-checking and subscription decisions. Questionnaire responses and subscription rates showed a decline in trust only for detailed AI disclosures, whereas source-checking behavior increased for both one-line and detailed disclosures, with the effect being more pronounced for detailed disclosures. Insights from semi-structured interviews suggest that source-checking behavior was primarily driven by interest in the topic, followed by trust, whereas trust was the main factor influencing subscription decisions. Around two-thirds of participants expressed a preference for detailed disclosures, while most participants who preferred one-line indicated a need for detail-on-demand disclosure formats. Our findings show that not all AI disclosures lead to a transparency dilemma, but instead reflect a trade-off between readers' desire for more transparency and their trust in AI-assisted news content.
As AI-generated health information proliferates online and becomes increasingly indistinguishable from human-sourced information, it becomes critical to understand how people trust and label such content, especially when the information is inaccurate. We conducted two complementary studies: (1) a mixed-methods survey (N=142) employing a 2 (source: Human vs. LLM) × 2 (label: Human vs. AI) × 3 (type: General, Symptom, Treatment) design, and (2) a within-subjects lab study (N=40) incorporating eye-tracking and physiological sensing (ECG, EDA, skin temperature). Participants were presented with health information varying by source-label combinations and asked to rate their trust, while their gaze behavior and physiological signals were recorded. We found that LLM-generated information was trusted more than human-generated content, whereas information labeled as human was trusted more than that labeled as AI. Trust remained consistent across information types. Eye-tracking and physiological responses varied significantly by source and label. Machine learning models trained on these behavioral and physiological features predicted binary self-reported trust levels with 73 % accuracy and information source with 65 % accuracy. Our findings demonstrate that adding transparency labels to online health information modulates trust. Behavioral and physiological features show potential to verify trust perceptions and indicate if additional transparency is needed.
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As AI-generated health information proliferates online and becomes increasingly indistinguishable from human-sourced information, it becomes critical to understand how people trust and label such content, especially when the information is inaccurate. We conducted two complementary studies: (1) a mixed-methods survey (N=142) employing a 2 (source: Human vs. LLM) × 2 (label: Human vs. AI) × 3 (type: General, Symptom, Treatment) design, and (2) a within-subjects lab study (N=40) incorporating eye-tracking and physiological sensing (ECG, EDA, skin temperature). Participants were presented with health information varying by source-label combinations and asked to rate their trust, while their gaze behavior and physiological signals were recorded. We found that LLM-generated information was trusted more than human-generated content, whereas information labeled as human was trusted more than that labeled as AI. Trust remained consistent across information types. Eye-tracking and physiological responses varied significantly by source and label. Machine learning models trained on these behavioral and physiological features predicted binary self-reported trust levels with 73 % accuracy and information source with 65 % accuracy. Our findings demonstrate that adding transparency labels to online health information modulates trust. Behavioral and physiological features show potential to verify trust perceptions and indicate if additional transparency is needed.
Disability, Differences, and Diversity
Revisiting Inclusive Design and Access
Conference paper
(2026)
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Himanshu Verma, Giulia Barbareschi, Sophia Ppali, Kathrin Gerling, Maartje De Meulder, Judith Good, Jatinder Singh, Pablo Cesar, Alessandro Bozzon, More Authors
Over 1.3 billion people worldwide live with long-term disabilities, yet many still face systemic exclusion despite advances in accessibility policy and technology. New regulations such as the EU Accessibility Act demand comprehensive transitions, but compliance risks becoming a superficial “checklist” exercise rather than fostering meaningful inclusion. For the HCI community, this moment calls for rethinking our approaches to participation, technology, ethics, and policy. In this meetup, we bring together researchers, practitioners, and advocates to revisit inclusive design through four themes: rethinking inclusive methodologies, disentangling technological challenges, unpacking ethical implications, and navigating policy opportunities. Through interactive mapping activities, participants will share practices, identify collaboration opportunities, and co-develop future directions. Our goal is to build cross-disciplinary connections and create actionable approaches that move beyond compliance toward holistic inclusion, ensuring that accessibility remains central to HCI research and practice.
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Over 1.3 billion people worldwide live with long-term disabilities, yet many still face systemic exclusion despite advances in accessibility policy and technology. New regulations such as the EU Accessibility Act demand comprehensive transitions, but compliance risks becoming a superficial “checklist” exercise rather than fostering meaningful inclusion. For the HCI community, this moment calls for rethinking our approaches to participation, technology, ethics, and policy. In this meetup, we bring together researchers, practitioners, and advocates to revisit inclusive design through four themes: rethinking inclusive methodologies, disentangling technological challenges, unpacking ethical implications, and navigating policy opportunities. Through interactive mapping activities, participants will share practices, identify collaboration opportunities, and co-develop future directions. Our goal is to build cross-disciplinary connections and create actionable approaches that move beyond compliance toward holistic inclusion, ensuring that accessibility remains central to HCI research and practice.
Perceptual quality assessment of Dynamic Point Cloud (DPC) contents plays an important role in various Virtual Reality (VR) applications that involve human beings as the end user. Understanding and modeling perceptual quality assessment is greatly enriched by insights from visual attention. However, incorporating aspects of visual attention in DPC quality models is largely unexplored, as ground-truth visual attention data are scarcely available. Besides, testing methods and procedures for collecting visual attention data are still to be agreed on. This article presents a dataset containing subjective opinion scores and visual attention maps of DPCs, collected in a VR environment using eye-tracking technology. Both the quality score and eye-tracking data were collected during a subjective quality assessment experiment, in which subjects were instructed to watch and rate DPCs at various degradation levels under 6 Degrees of Freedom (DoF) inspection, using a head-mounted display. Qualitative interview analysis was also conducted after the experiment. The dataset consists of 50 DPCs, including 5 reference DPCs, with each reference encoded at 3 distortion levels using 3 different codecs (namely G-PCC, V-PCC, CWI-PCL), amounting to a total of 9 degraded version per reference. Additionally, it incorporates 1,000 gaze trials from 40 participants, yielding a total of 15,000 visual attention maps across all the DPCs. We additionally benchmark objective quality metrics originally designed for static point clouds, evaluating their performance in our dataset using two temporal pooling strategies. Furthermore, we employ the visual attention data that are retrieved during our experiment to evaluate whether the performance of widely used objective quality metrics is improved by considering subjective measurements of visual attention. This dataset establishes a link between quality assessment and visual attention within the context of DPC. Moreover, thematic analysis of the interviews helps uncover user behavior and factors impacting perceptual quality for DPC in 6 DoF. This work deepens our understanding of DPC quality assessment and visual attention, driving progress in the realm of VR experiences and perception.
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Perceptual quality assessment of Dynamic Point Cloud (DPC) contents plays an important role in various Virtual Reality (VR) applications that involve human beings as the end user. Understanding and modeling perceptual quality assessment is greatly enriched by insights from visual attention. However, incorporating aspects of visual attention in DPC quality models is largely unexplored, as ground-truth visual attention data are scarcely available. Besides, testing methods and procedures for collecting visual attention data are still to be agreed on. This article presents a dataset containing subjective opinion scores and visual attention maps of DPCs, collected in a VR environment using eye-tracking technology. Both the quality score and eye-tracking data were collected during a subjective quality assessment experiment, in which subjects were instructed to watch and rate DPCs at various degradation levels under 6 Degrees of Freedom (DoF) inspection, using a head-mounted display. Qualitative interview analysis was also conducted after the experiment. The dataset consists of 50 DPCs, including 5 reference DPCs, with each reference encoded at 3 distortion levels using 3 different codecs (namely G-PCC, V-PCC, CWI-PCL), amounting to a total of 9 degraded version per reference. Additionally, it incorporates 1,000 gaze trials from 40 participants, yielding a total of 15,000 visual attention maps across all the DPCs. We additionally benchmark objective quality metrics originally designed for static point clouds, evaluating their performance in our dataset using two temporal pooling strategies. Furthermore, we employ the visual attention data that are retrieved during our experiment to evaluate whether the performance of widely used objective quality metrics is improved by considering subjective measurements of visual attention. This dataset establishes a link between quality assessment and visual attention within the context of DPC. Moreover, thematic analysis of the interviews helps uncover user behavior and factors impacting perceptual quality for DPC in 6 DoF. This work deepens our understanding of DPC quality assessment and visual attention, driving progress in the realm of VR experiences and perception.
MASSXR 2025
The 3rd Workshop on Multi-modal Affective and Social Behavior Analysis and Synthesis in Extended Reality (Affiliated with IEEE VR 2025)
Conference paper
(2025)
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Megha Quamara, Oya Celiktutan, Luca Viganò, Aniket Bera, Pablo Cesar, Funda Durupinar, Aline Normoyle, Chirag Raman, Zerrin Yumak
The objective of MASSXR 2025, the 3rd Workshop on Multi-modal Affective and Social Behavior Analysis and Synthesis in Extended Reality, was to bring together researchers and practitioners from fields including cybersecurity, human-computer interaction, computer graphics/animation, multi-modal machine learning, Artificial Intelligence (AI), data privacy, and socio-technical studies to discuss the state of security in Extended Reality (XR), as well as future directions and opportunities. Through this, it aimed to achieve adaptive, context-aware security measures that are both technically robust and aligned with user trust and understanding. The workshop provided an opportunity to foster collaborative research efforts and advance the state of secure social interactions within XR, setting a foundation for future innovations in the field.
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The objective of MASSXR 2025, the 3rd Workshop on Multi-modal Affective and Social Behavior Analysis and Synthesis in Extended Reality, was to bring together researchers and practitioners from fields including cybersecurity, human-computer interaction, computer graphics/animation, multi-modal machine learning, Artificial Intelligence (AI), data privacy, and socio-technical studies to discuss the state of security in Extended Reality (XR), as well as future directions and opportunities. Through this, it aimed to achieve adaptive, context-aware security measures that are both technically robust and aligned with user trust and understanding. The workshop provided an opportunity to foster collaborative research efforts and advance the state of secure social interactions within XR, setting a foundation for future innovations in the field.
Conference paper
(2025)
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Karthikeya Puttur Venkatraj, Sophie Morosoli, Hannes Cools, Laurens Naudts, De Vreese Claes De Vreese, Natali Helberger, Pablo Cesar, Abdallah El Ali
Artificial Intelligence (AI) is revolutionizing the way content is produced and integrated into journalistic workflows. The EU AI act's Article 50 sets up transparency requirements aimed at encouraging the adoption and disclosure of AI in an ethical and responsible manner. In this study, we organized focus group interviews with Dutch citizens (N=21) to understand their expectations and needs regarding AI disclosures in the context of news production and journalism. These conversations are essential to understand if legal and regulatory policies are grounded in real-world experiences of citizens, and adequately address their concerns and enhance their digital interactions. We found that citizens predominantly favor disclosures of AI usage in journalistic content, in the form of (1) source references, (2) visual indicators (logos/watermarks) and (3) have varying preferences regarding information presentation and interaction modalities. Our findings highlight the need for interdisciplinary approaches to align standardization efforts with AI disclosures for news media.
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Artificial Intelligence (AI) is revolutionizing the way content is produced and integrated into journalistic workflows. The EU AI act's Article 50 sets up transparency requirements aimed at encouraging the adoption and disclosure of AI in an ethical and responsible manner. In this study, we organized focus group interviews with Dutch citizens (N=21) to understand their expectations and needs regarding AI disclosures in the context of news production and journalism. These conversations are essential to understand if legal and regulatory policies are grounded in real-world experiences of citizens, and adequately address their concerns and enhance their digital interactions. We found that citizens predominantly favor disclosures of AI usage in journalistic content, in the form of (1) source references, (2) visual indicators (logos/watermarks) and (3) have varying preferences regarding information presentation and interaction modalities. Our findings highlight the need for interdisciplinary approaches to align standardization efforts with AI disclosures for news media.
PointPCA+
A Full-reference Point Cloud Quality Assessment Metric with PCA-based Features
This paper introduces an enhanced Point Cloud Quality Assessment (PCQA) metric, termed PointPCA+, as an extension of PointPCA, with a focus on computational simplicity and feature richness. PointPCA+ refines the original PCA-based descriptors by employing Principal Component Analysis (PCA) solely on geometry data; additionally, the texture descriptors are refined through a direct application of the function on YCbCr values, enhancing the efficiency of computation. The metric combines geometry and texture features, capturing local shape and appearance properties, through a learning-based fusion to generate a total quality score. Prior to fusion, a feature selection module is incorporated to identify the most effective features from a proposed super-set. Experimental results demonstrate the high predictive performance of PointPCA+ against subjective ground truth scores obtained from four publicly available datasets. The metric consistently outperforms state-of-the-art solutions, offering valuable insights into the design of similarity measurements and the effectiveness of handcrafted features across various distortion types.
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This paper introduces an enhanced Point Cloud Quality Assessment (PCQA) metric, termed PointPCA+, as an extension of PointPCA, with a focus on computational simplicity and feature richness. PointPCA+ refines the original PCA-based descriptors by employing Principal Component Analysis (PCA) solely on geometry data; additionally, the texture descriptors are refined through a direct application of the function on YCbCr values, enhancing the efficiency of computation. The metric combines geometry and texture features, capturing local shape and appearance properties, through a learning-based fusion to generate a total quality score. Prior to fusion, a feature selection module is incorporated to identify the most effective features from a proposed super-set. Experimental results demonstrate the high predictive performance of PointPCA+ against subjective ground truth scores obtained from four publicly available datasets. The metric consistently outperforms state-of-the-art solutions, offering valuable insights into the design of similarity measurements and the effectiveness of handcrafted features across various distortion types.
PhysioDrum
Bridging Physical and Digital Realms in Immersive Musical Interaction
The Internet of Multisensory, Multimedia, and Musical Things (Io3MT) bridges computer science, humanities, and arts, fostering transmedia services and creative applications. This demo research applies these principles alongside extended reality (XR) to enhance PhysioDrum, an immersive, multimodal system that blends physical and digital aspects to expand musical expression in virtual environments. Using a smart musical instrument (SMI) and electronic pedals as interfaces, users interact with a virtual drum kit through gestures while receiving haptic feedback. By integrating sound and multimedia elements, PhysioDrum aims to reduces cognitive load and the learning curve, merging traditional drumming practices with immersive XR. The demo emphasizes design strategies that enhance playability, accessibility, and creative potential for users of all skill levels.
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The Internet of Multisensory, Multimedia, and Musical Things (Io3MT) bridges computer science, humanities, and arts, fostering transmedia services and creative applications. This demo research applies these principles alongside extended reality (XR) to enhance PhysioDrum, an immersive, multimodal system that blends physical and digital aspects to expand musical expression in virtual environments. Using a smart musical instrument (SMI) and electronic pedals as interfaces, users interact with a virtual drum kit through gestures while receiving haptic feedback. By integrating sound and multimedia elements, PhysioDrum aims to reduces cognitive load and the learning curve, merging traditional drumming practices with immersive XR. The demo emphasizes design strategies that enhance playability, accessibility, and creative potential for users of all skill levels.
Conference paper
(2025)
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Julie Williamson, Irene Viola, Silvia Rossi, John Williamson, Ross Johnstone, Thomas Röggla, David A. Shamma, Pablo Cesar
Virtual environments make it possible to connect and collaborate in social immersive realities, but there are still open questions about the influence of their design on the user experience. We conducted a study with 48 participants divided into groups of 6, completing conversational tasks in an instrumented virtual environment. Using a mix-methods approach, combining qualitative and quantitative research methods (interviews, questionnaires, conversation and movement analysis), we compared between two virtual environment designs. We found that the social density (or effective capacity) of the designed virtual environment influenced the quality of interaction between the participants.
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Virtual environments make it possible to connect and collaborate in social immersive realities, but there are still open questions about the influence of their design on the user experience. We conducted a study with 48 participants divided into groups of 6, completing conversational tasks in an instrumented virtual environment. Using a mix-methods approach, combining qualitative and quantitative research methods (interviews, questionnaires, conversation and movement analysis), we compared between two virtual environment designs. We found that the social density (or effective capacity) of the designed virtual environment influenced the quality of interaction between the participants.
Curating with Technology
How to Bring Old Fashion Back to Life in Museum Exhibitions
Social museums, constantly challenged by changing visitors’ needs, are beginning to adopt technology in order to enrich guests’ experiences. However, designing an exhibition that incorporates digital tools is not easy - it requires a new approach and expertise in both cultural heritage and technology. At the same time, there is a lack of clear guidance on how to effectively design digitally enhanced exhibitions. In this work we follow a human-centric approach, which engages both museum curators and technical experts throughout all stages of the exhibition design. The process, presented in Figure 1, starts with a focus group with curators (N = 4) aiming at understanding the current museum challenges and exploring ways to address them. Based on the workshop results, an initial design is prepared, which is later reiterated during 8 co-design sessions (N = 15). The final design is validated during the validation session (N = 6), resulting in a set of requirements important for social VR fashion exhibition design. The study provides insights for curators into how exhibitions of the future could look like and guidelines on how to design such an exhibition, engaging the technology team throughout the whole process.
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Social museums, constantly challenged by changing visitors’ needs, are beginning to adopt technology in order to enrich guests’ experiences. However, designing an exhibition that incorporates digital tools is not easy - it requires a new approach and expertise in both cultural heritage and technology. At the same time, there is a lack of clear guidance on how to effectively design digitally enhanced exhibitions. In this work we follow a human-centric approach, which engages both museum curators and technical experts throughout all stages of the exhibition design. The process, presented in Figure 1, starts with a focus group with curators (N = 4) aiming at understanding the current museum challenges and exploring ways to address them. Based on the workshop results, an initial design is prepared, which is later reiterated during 8 co-design sessions (N = 15). The final design is validated during the validation session (N = 6), resulting in a set of requirements important for social VR fashion exhibition design. The study provides insights for curators into how exhibitions of the future could look like and guidelines on how to design such an exhibition, engaging the technology team throughout the whole process.
Virtual coaches in virtual reality (VR) offer scalable mental health treatment without an on-site therapist, yet their impact on psychophysiological responses remains unclear. We examine how VR content and coach design influence physiological measures, such as heart rate (HR) and electrodermal activity (EDA), in a therapeutic setting. 120 participants with a fear of heights interacted with a virtual coach that varied in facial warmth (with/without) and affirmative nods (with/without) during a virtual consultation, followed by a virtual height exposure. Physiological responses were recorded. Virtual heights exposure elicited significantly higher HR (p < 0.001, r = 0.347) and EDA (p = 0.003, r = 0.292), but also increased heart rate variability (HRV, p = 0.005, r = 0.272) compared to the VR consultation. Warm facial expressions increased EDA peak amplitudes (p = 0.043, ηP2 = 0.574) during the consultation and raised HRV during height exposure (p = 0.036, ηP2 = 0.041). This study highlights VR coach design’s impact on physiological responses, emphasising the need for thoughtful emotional design to enhance therapeutic outcomes in automated VR therapies.
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Virtual coaches in virtual reality (VR) offer scalable mental health treatment without an on-site therapist, yet their impact on psychophysiological responses remains unclear. We examine how VR content and coach design influence physiological measures, such as heart rate (HR) and electrodermal activity (EDA), in a therapeutic setting. 120 participants with a fear of heights interacted with a virtual coach that varied in facial warmth (with/without) and affirmative nods (with/without) during a virtual consultation, followed by a virtual height exposure. Physiological responses were recorded. Virtual heights exposure elicited significantly higher HR (p < 0.001, r = 0.347) and EDA (p = 0.003, r = 0.292), but also increased heart rate variability (HRV, p = 0.005, r = 0.272) compared to the VR consultation. Warm facial expressions increased EDA peak amplitudes (p = 0.043, ηP2 = 0.574) during the consultation and raised HRV during height exposure (p = 0.036, ηP2 = 0.041). This study highlights VR coach design’s impact on physiological responses, emphasising the need for thoughtful emotional design to enhance therapeutic outcomes in automated VR therapies.
The advent in our daily life of Extended Reality (XR) technologies, such as Virtual and Augmented Reality, has led to the rise of user-centric systems, offering higher level of interaction and presence in virtual environments. In this context, understanding the actual interactivity of users is still an open challenge and a key step to enabling user-centric system. In this work, our goal is to construct an efficient clustering tool for 6 df navigation trajectories by extending the applicability of existing behavioural tool. Specifically, we first compare the navigation in 6 df with its 3 df counterpart, highlighting the main differences and novelties. Then, we investigate new metrics aimed at better modelling behavioural similarities between users in a 6 df system. More concretely, we define and compare 11 similarity metrics which are based on different distance features (i.e., user positions in the 3D space, user viewing directions) and distance measurements (i.e., Euclidean, Geodesic, angular distance). Our solutions are validated and tested on real navigation paths of users interacting with dynamic volumetric media in both 6 df Virtual Reality and Augmented Reality conditions. Results show that metrics based on both user position and viewing direction better perform in detecting user similarity while navigating in a 6 df system. Such easy-to-use but robust metrics allow us to answer a fundamental question for user-centric systems: ‘How do we detect if users look at the same content in 6 df?’, opening the gate to new solutions based on users interactivity, such as viewport prediction, live streaming services optimised based on users behaviour but also for user-based quality assessment methods.
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The advent in our daily life of Extended Reality (XR) technologies, such as Virtual and Augmented Reality, has led to the rise of user-centric systems, offering higher level of interaction and presence in virtual environments. In this context, understanding the actual interactivity of users is still an open challenge and a key step to enabling user-centric system. In this work, our goal is to construct an efficient clustering tool for 6 df navigation trajectories by extending the applicability of existing behavioural tool. Specifically, we first compare the navigation in 6 df with its 3 df counterpart, highlighting the main differences and novelties. Then, we investigate new metrics aimed at better modelling behavioural similarities between users in a 6 df system. More concretely, we define and compare 11 similarity metrics which are based on different distance features (i.e., user positions in the 3D space, user viewing directions) and distance measurements (i.e., Euclidean, Geodesic, angular distance). Our solutions are validated and tested on real navigation paths of users interacting with dynamic volumetric media in both 6 df Virtual Reality and Augmented Reality conditions. Results show that metrics based on both user position and viewing direction better perform in detecting user similarity while navigating in a 6 df system. Such easy-to-use but robust metrics allow us to answer a fundamental question for user-centric systems: ‘How do we detect if users look at the same content in 6 df?’, opening the gate to new solutions based on users interactivity, such as viewport prediction, live streaming services optimised based on users behaviour but also for user-based quality assessment methods.
UVG-CWI-DQPC
Dual-Quality Point Cloud Dataset for Volumetric Video Applications
Conference paper
(2025)
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Guillaume Gautier, Xuemei Zhou, Thong Nguyen, Jack Jansen, Louis Fréneau, Marko Viitanen, Uyen Phan, Jani Käpylä, Pablo Cesar, More authors...
Volumetric video is a key enabler of immersive extended reality (XR) experiences and is often represented using point clouds for their structural simplicity. However, capturing volumetric content through multi-view acquisition and depth sensing poses many challenges, such as occlusions and depth mismatches. To foster research in this field, we introduce a unique dual-quality point cloud dataset, named UVG-CWI-DQPC, which is designed to support the development of point cloud enhancement, compression, and quality assessment. Our dataset includes 12 dynamic sequences captured simultaneously by: 1) a high-end capture system producing high-fidelity point clouds with extensive processing; and 2) a consumer-grade capture system relying on affordable RGB-D cameras, lightweight processing, and open-source tools. For each sequence, our dataset provides ground-truth point clouds from the high-end capture system and raw RGB-D footage from the consumer-grade capture system, along with calibration data and tools for point cloud generation. This dual-quality setup enables direct comparison and benchmarking of algorithms for densification, occlusion removal, registration, and quality enhancement. Our dataset is publicly available under a permissive license to support reproducible research and standardization work in Moving Picture Experts Group (MPEG) and 3rd Generation Partnership Project (3GPP).
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Volumetric video is a key enabler of immersive extended reality (XR) experiences and is often represented using point clouds for their structural simplicity. However, capturing volumetric content through multi-view acquisition and depth sensing poses many challenges, such as occlusions and depth mismatches. To foster research in this field, we introduce a unique dual-quality point cloud dataset, named UVG-CWI-DQPC, which is designed to support the development of point cloud enhancement, compression, and quality assessment. Our dataset includes 12 dynamic sequences captured simultaneously by: 1) a high-end capture system producing high-fidelity point clouds with extensive processing; and 2) a consumer-grade capture system relying on affordable RGB-D cameras, lightweight processing, and open-source tools. For each sequence, our dataset provides ground-truth point clouds from the high-end capture system and raw RGB-D footage from the consumer-grade capture system, along with calibration data and tools for point cloud generation. This dual-quality setup enables direct comparison and benchmarking of algorithms for densification, occlusion removal, registration, and quality enhancement. Our dataset is publicly available under a permissive license to support reproducible research and standardization work in Moving Picture Experts Group (MPEG) and 3rd Generation Partnership Project (3GPP).
Conference paper
(2025)
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Simone Ooms, Minha Lee, Ekaterina R. Stepanova, Pablo Cesar, Abdallah El Ali
Encounters with virtual agents currently lack the haptic viscerality of human contact. While digital biosignal communication can mediate such virtual social interactions, how artificial haptic biosignals influence users’ personal space during Virtual Reality (VR) experiences is unknown. Designing vibrotactile heartbeats and thermally-actuated body temperature, we ran a within-subjects study (N=31) to investigate feedback (Thermal, Vibration, Thermal+Vibration, None) and agent stories (Negative, Neutral, Positive) on objective and subjective interpersonal distance (IPD), perceived arousal and comfort, presence, and post-experience responses. Findings showed that thermal feedback decreased objective but not subjective IPD, whereas vibrotactile heartbeats (signaling agent’s closeness) increased both while heightening arousal and discomfort. Agents’ stories did not affect IPD, arousal, or comfort. Our qualitative findings shed light on signal ambiguity and presence constructs within VR-based haptic stimulation. We contribute insights into artificial biosignals and their influence on VR proxemics, with cautionary considerations should the boundaries blur between physical and virtual touch.
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Encounters with virtual agents currently lack the haptic viscerality of human contact. While digital biosignal communication can mediate such virtual social interactions, how artificial haptic biosignals influence users’ personal space during Virtual Reality (VR) experiences is unknown. Designing vibrotactile heartbeats and thermally-actuated body temperature, we ran a within-subjects study (N=31) to investigate feedback (Thermal, Vibration, Thermal+Vibration, None) and agent stories (Negative, Neutral, Positive) on objective and subjective interpersonal distance (IPD), perceived arousal and comfort, presence, and post-experience responses. Findings showed that thermal feedback decreased objective but not subjective IPD, whereas vibrotactile heartbeats (signaling agent’s closeness) increased both while heightening arousal and discomfort. Agents’ stories did not affect IPD, arousal, or comfort. Our qualitative findings shed light on signal ambiguity and presence constructs within VR-based haptic stimulation. We contribute insights into artificial biosignals and their influence on VR proxemics, with cautionary considerations should the boundaries blur between physical and virtual touch.
Conference paper
(2025)
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Irene Viola, Moonisa Ahsan, Olga Chatzifoti, Atanas Yonkov, Eleni Oikonomou, Ioannis Radin, Paweł Maka, Abderrahmane Issam, Pablo Cesar
Recent technological developments on AI and immersive media are transforming the artistic landscape, providing novel mechanisms for artists and audiences. Following a human-centric approach, together with a theatre company in Greece, this paper investigates how subtitle placement affects user experience and cognitive load in a live theatre performance enhanced by AR glasses. To do so, we design and develop a system for displaying subtitles in VR and AR. We evaluated the system in two conditions (N = 19;N = 12), both in a controlled environment (VR) and an actual theatre (AR). In the latter, we integrate AI solutions to provide automatic captioning and translation in real time, and VFX to further augment the experience. Our quantitative and qualitative results showed no difference between subtitle placements in terms of cognitive load and user experience, with users equally liking the two proposed approaches. Results also highlighted the perceived usefulness of AR to enhance theatre performances, indicating new paths for wider accessibility and further immersion.
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Recent technological developments on AI and immersive media are transforming the artistic landscape, providing novel mechanisms for artists and audiences. Following a human-centric approach, together with a theatre company in Greece, this paper investigates how subtitle placement affects user experience and cognitive load in a live theatre performance enhanced by AR glasses. To do so, we design and develop a system for displaying subtitles in VR and AR. We evaluated the system in two conditions (N = 19;N = 12), both in a controlled environment (VR) and an actual theatre (AR). In the latter, we integrate AI solutions to provide automatic captioning and translation in real time, and VFX to further augment the experience. Our quantitative and qualitative results showed no difference between subtitle placements in terms of cognitive load and user experience, with users equally liking the two proposed approaches. Results also highlighted the perceived usefulness of AR to enhance theatre performances, indicating new paths for wider accessibility and further immersion.
Emotion recognition systems are typically trained to classify a given psychophysiological state into emotion categories. Current platforms for emotion ground-truth collection show limitations for real-world scenarios of long-duration content (e.g. >10 minutes), namely: 1) Real-time annotation tools are distracting and become exhausting; 2) Perform retrospective annotation of the whole content in bulk (providing highly coarse annotations); or 3) Are used by external experts (depending on the number of annotators and their subjective experience). We explore a novel approach, the EmotiphAI Annotator, that allows undisturbed content visualisation and simplifies the annotation process by using segmentation algorithms that select brief clips for emotional annotation retrospectively. We compare three methods for content segmentation based on physiological data (Electrodermal Activity (EDA), emotion-based), scene (time-based), and random (control) selection. The EmotiphAI Annotator attained a B+ System Usability Scale score and low-average mental workload as per the NASA Task Load Index (40%). The reliability of the self-report was analysed by the inter-rater agreement (STD < 0.75), coherence across time segmentation methods (STD < 0.17), comparison against the state-of-the-art ground truth (STD < 0.7), and correlation to EDA (>0.3 to 0.8), where the EDA-based method obtained the overall best performance.
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Emotion recognition systems are typically trained to classify a given psychophysiological state into emotion categories. Current platforms for emotion ground-truth collection show limitations for real-world scenarios of long-duration content (e.g. >10 minutes), namely: 1) Real-time annotation tools are distracting and become exhausting; 2) Perform retrospective annotation of the whole content in bulk (providing highly coarse annotations); or 3) Are used by external experts (depending on the number of annotators and their subjective experience). We explore a novel approach, the EmotiphAI Annotator, that allows undisturbed content visualisation and simplifies the annotation process by using segmentation algorithms that select brief clips for emotional annotation retrospectively. We compare three methods for content segmentation based on physiological data (Electrodermal Activity (EDA), emotion-based), scene (time-based), and random (control) selection. The EmotiphAI Annotator attained a B+ System Usability Scale score and low-average mental workload as per the NASA Task Load Index (40%). The reliability of the self-report was analysed by the inter-rater agreement (STD < 0.75), coherence across time segmentation methods (STD < 0.17), comparison against the state-of-the-art ground truth (STD < 0.7), and correlation to EDA (>0.3 to 0.8), where the EDA-based method obtained the overall best performance.
Deciphering Perceptual Quality in Colored Point Cloud
Prioritizing Geometry or Texture Distortion?
Point clouds represent one of the prevalent formats for 3D content. Distortions introduced at various stages in the point cloud processing pipeline affect the visual quality, altering their geometric composition, texture information, or both. Understanding and quantifying the impact of the distortion domain on visual quality is vital to driving rate optimization and guiding post-processing steps to improve the quality of experience. In this paper, we propose a multi-task guided multi-modality no reference metric (M3-Unity), which utilizes 4 types of modalities across attributes and dimensionalities to represent point clouds. An attention mechanism establishes inter/intra associations among 3D/2D patches, which can complement each other, yielding local and global features, to fit the highly nonlinear property of the human vision system. A multi-task decoder involving distortion type classification selects the best association among 4 modalities, aiding the regression task and enabling the in-depth analysis of the interplay between geometrical and textural distortions. Furthermore, our framework design and attention strategy enable us to measure the impact of individual attributes and their combinations, providing insights into how these associations contribute particularly in relation to distortion type. Extensive experimental results on 4 datasets consistently outperform the state-of-the-art metrics by a large margin.
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Point clouds represent one of the prevalent formats for 3D content. Distortions introduced at various stages in the point cloud processing pipeline affect the visual quality, altering their geometric composition, texture information, or both. Understanding and quantifying the impact of the distortion domain on visual quality is vital to driving rate optimization and guiding post-processing steps to improve the quality of experience. In this paper, we propose a multi-task guided multi-modality no reference metric (M3-Unity), which utilizes 4 types of modalities across attributes and dimensionalities to represent point clouds. An attention mechanism establishes inter/intra associations among 3D/2D patches, which can complement each other, yielding local and global features, to fit the highly nonlinear property of the human vision system. A multi-task decoder involving distortion type classification selects the best association among 4 modalities, aiding the regression task and enabling the in-depth analysis of the interplay between geometrical and textural distortions. Furthermore, our framework design and attention strategy enable us to measure the impact of individual attributes and their combinations, providing insights into how these associations contribute particularly in relation to distortion type. Extensive experimental results on 4 datasets consistently outperform the state-of-the-art metrics by a large margin.