1,720,952 research outputs found
Social virtual reality (VR) applications and user experiences
No full textVirtual reality (VR) is the experience in a simulated interactive virtual space. It provides synthetic sensory feedback to users' actions that can physically and mentally immerse the users. Social VR is one type of VR system that allows multiple users to join a collaborative virtual environment and communicate with each other, usually by means of visual and audio cues. The virtual space can be a computer-generated 3D scene or a 360° scene captured by an omnidirectional camera. Each user is represented as a computer-generated avatar or using a virtual representation based on live capture. Social VR has shown to be a promising solution to enrich remote communication experiences in the face of increasing pressures to reduce travel and work remotely. In this chapter, we will showcase the design, implementation, and real-world deployment of a series of social VR applications, which are developed for immersive interactive communication and collaboration for multiple domains (e.g., healthcare, cultural heritage). Moreover, we discuss the experimental protocols for evaluating user experiences in this new medium.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia Computin
Evaluation of point cloud features for no-reference visual quality assessment
No full textThe development and widespread adoption of immersive XR applications has led to a renewed interest in representations that are capable of reproducing real-world objects and scenes with high fidelity. Among such representations, point clouds have attracted the interest of industry and academia alike, and new compression solutions have been developed to facilitate their adoption in mainstream applications. To ensure the best quality of experience for the end-user in limited bandwidth scenarios, new full-reference objective quality metrics have been proposed, promoting features designed specifically for point cloud contents. However, the performance of such features to predict the quality of point cloud contents when the reference is not available is largely unexplored. In this paper, we evaluate the performance of features commonly used to model point cloud distortions in a no-reference framework. The obtained features are integrated into a quality value through a support vector regression model. Results demonstrate the potential of full-reference features for no-reference assessment. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia Computin
Governance and economic growth
Full text linkBecause protection of property rights cannot be appropriated by any individual, it is widely recognized as being the state's responsibility. Moreover, recent empirical evidence suggests that protection of property rights leads to higher investment levels and faster growth. The extent of property rights protection differs significantly across countries. The author integrates the emergence of property rights within a simple growth framework. Drawing on North (1990), he presents a model where economic performance and enforcement of property rights may reinforce each other.Initial conditions determine the economy's convergence to a high-income or a low-income steady state. Existing empirical evidence offers tentative support for this theory.Judicial System Reform,Labor Policies,Economic Theory&Research,Environmental Economics&Policies,Common Property Resource Development,Economic Theory&Research,Inequality,Common Property Resource Development,Environmental Economics&Policies,Governance Indicators
Weakly-supervised Learning for Fine-grained Emotion Recognition using Physiological Signals
No full textInstead of predicting just one emotion for one activity (e.g., video watching), fine-grained emotion recognition enables more temporally precise recognition. Previous works on fine-grained emotion recognition require segment-by-segment, fine-grained emotion labels to train the recognition algorithm. However, experiments to collect these labels are costly and time-consuming compared with only collecting one emotion label after the user watched that stimulus (i.e., the post-stimuli emotion labels). To recognize emotions at a finer granularity level when trained with only post-stimuli labels, we propose an emotion recognition algorithm based on Deep Multiple Instance Learning (EDMIL) using physiological signals. EDMIL recognizes fine-grained valence and arousal (V-A) labels by identifying which instances represent the post-stimuli V-A annotated by users after watching the videos. Instead of fully-supervised training, the instances are weakly-supervised by the post-stimuli labels in the training stage. The V-A of instances are estimated by the instance gains, which indicate the probability of instances to predict the post-stimuli labels. We tested EDMIL on three different datasets, CASE, MERCA and CEAP-360VR, collected in three different environments: desktop, mobile and HMD-based Virtual Reality, respectively. Recognition results validated with the fine-grained V-A self-reports show that for subject-independent 3-class classification (high/neutral/low), EDMIL obtains promising recognition accuracies: 75.63% and 79.73% for V-A on CASE, 70.51% and 67.62% for V-A on MERCA and 65.04% and 67.05% for V-A on CEAP-360VR. Our ablation study shows that all components of EDMIL contribute to both the classification and regression tasks. Our experiments also show that (1) compared with fully-supervised learning, weakly-supervised learning can reduce the problem of overfitting caused by the temporal mismatch between fine-grained annotations and physiological signals, (2) instance segment lengths between 1-2 s result in the highest recognition accuracies and (3) EDMIL performs best if post-stimuli annotations consist of less than 30% or more than 60% of the entire video watching.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia ComputingIntelligent System
Subjective QoE Evaluation of User-Centered Adaptive Streaming of Dynamic Point Clouds
No full textTechnological advances in head-mounted displays and novel real-time 3D acquisition and reconstruction solutions have fostered the development of 6 Degrees of Freedom (6DoF) teleimmersive systems for social VR applications. Point clouds have emerged as a popular format for such applications, owing to their simplicity and versatility; yet, dense point cloud contents are too large to deliver directly over bandwidth-limited networks. In this context, user-adaptive delivery mechanisms are a promising solution to exploit the increased range of motion offered by 6DoF VR applications to yield gains in perceived quality of 3D point cloud user representations, while reducing their bandwidth requirements. In this paper, we perform a user study in VR to quantify the gains adaptive tile selection strategies can bring with respect to non-adaptive solutions. In particular, we define an auxiliary utility function, we employ established methods from the literature and newly-proposed schemes for distributing the bit budget across the tiles, and we evaluate them together with non-adaptive streaming baselines through subjective QoE assessment. Results confirm that considerable gains can be obtained with user-adaptive streaming, achieving bit rate gains of up to 65% with respect to a non-adaptive approach to deliver comparable quality. Our analysis provides useful insights for the design and development of social VR applications. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia ComputingIntelligent System
FeelTheNews: Augmenting Affective Perceptions of News Videos with Thermal and Vibrotactile Stimulation
No full textEmotion plays a key role in the emerging wave of immersive, multi-sensory audience news engagement experiences. Since emotions can be triggered by somatosensory feedback, in this work we explore how augmenting news video watching with haptics can influence affective perceptions of news. Using a mixed-methods approach, we design and evaluate FeelTheNews, a prototype that combines vibrotactile and thermal stimulation (Matching, 70Hz/20° C, 200Hz/40° C) during news video watching. In a within-subjects study (N=20), we investigate the effects of haptic stimulation and video valence on perceived valence, emotion intensity, comfort, and overall haptic experiences. Findings showed: (a) news valence and emotion intensity ratings were not affected by haptics, (b) no stimulation was more comfortable than including stimulation, (c) attention and engagement with the news can override haptic sensations, and (d) users' perceived agency over their reactions is critical to avoid distrust. We contribute cautionary insights for haptic augmentation of the news watching experience.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia Computin
Uncovering seams in distributed play of tabletop role-playing games
No full textWe uncover how geographically distributed players of tabletop role-playing games engage narrative, ludic, and social aspects of play. Our existing understandings of tabletop role-playing games are centered around co-located play on physical tabletops. Yet, online play is increasingly popular. We interviewed 14 players, experienced with online virtual tabletops. Our findings reveal the seams—points where media, activities, and technology intersect—within virtual tabletop environments that enable distributed players to shift among collaborative storytelling, applying game rules and mechanics, and socially interacting with each other.Multimedia Computin
BreatheWithMe: Exploring Visual and Vibrotactile Displays for Social Breath Awareness during Colocated, Collaborative Tasks
No full textSharing breathing signals has the capacity to provide insights into hidden experiences and enhance interpersonal communication. However, it remains unclear how the modality of breath signals (visual, haptic) is socially interpreted during collaborative tasks. In this mixed-methods study, we design and evaluate BreatheWithMe, a prototype for real-time sharing and receiving of breathing signals through visual, vibrotactile, or visual-vibrotactile modalities. In a within-subjects study (15 pairs), we investigated the effects of modality on breathing synchrony, social presence, and overall user experience. Key findings showed: (a) there were no significant effects of visualization modality on breathing synchrony, only on deliberate music-driven synchronization; (b) visual modality was preferred over vibrotactile feedback, despite no differences across social presence dimensions; (c) BreatheWithMe was perceived to be an insightful window into others, however included data exposure and social acceptability concerns. We contribute insights into the design of multi-modal real-time breathing visualization systems for colocated, collaborative tasks.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia ComputingEmerging Material
Multi-Camera Registration for VR: A flexible, feature-based approach
No full textReal-time point cloud capturing and multiple depth camera 3D reconstruction are vital elements that bring real-time representations into a virtual world and provide an im- mersive experience which can be applied to develop VR/AR applications. To make this possible, camera calibration plays an essential role in providing important camera spa- tial information for 3D scene reconstruction. However, there are still many drawbacks left to improve on camera extrinsic parameters calculation in most existing systems: such as the procedure relies too much on extra calibration markers, or specific depth sensors may have complicated procedures that cannot easily be generalized to other depth sensors.To improve on this, we propose a markerless, feature-based pipeline for multiple cam- era re-calibration. This pipeline contains four main stages. It adopts feature descriptor extracting and matching to solve the issue of requiring additional markers, and the point cloud registration accuracy is improved by using point cloud segmentation and part selection. The experiment results obtained in this research show that this pipeline can calibrate four cameras with a single object (such as a chair, lamp) without the need for additional calibration markers. The extrinsic parameters calculated using this pipeline is more accurate and requires less processing time than originally. This pipeline provides the potential for further human point cloud capturing and camera calibration in real-time 3D reconstruction.VRTogetherComputer Scienc
Inter frame compression of 3D dynamic point clouds
No full textIn recent years Virtual Reality (VR) and Augmented Reality (AR) applications have seen a drastic increase in commercial popularity. Different representations have been used to create 3D reconstructions for AR and VR. Point clouds are one such representation that are characterized by their simplicity and versatility making them suitable for real time applications. However point clouds are unorganized and identifying redundancies to use for compressing them is challenging. For the compression of time varying or dynamic sequences it is critical to identify temporal redundancies that can be used to describe predictors and further compress streams of point clouds. Most of the previous research into point cloud compression relies on the octree data structure. However this approach was used on relatively sparse datasets. Recently, new dense photorealistic point cloud datasets have become available with the ongoing standardization activities on point cloud compression. To compress them using existing octree based codecs is computationally expensive as the tree depth required to achieve a reasonable level of detail is much higher than what was used previously. We propose a point cloud codec that terminates the octree at a fixed level of detail and encodes additional information in an enhancement layer. We also add inter prediction to the enhancement layer in order to gain further bit rate savings. We validate our codec by evaluating it in the framework set up by standardization organizations such as MPEG. We then demonstrate an improvement over the current MPEG anchor codec
- …
