Virtual Reality provides a new way of experiencing virtual content with unprecedented capabilities that have the potential of profoundly impact our society. However little is known about how this new scenario influences users’ perception. Our research efforts are targeted towards understanding viewers’ behavior in immersive VR environments.
With the proliferation of low-cost, consumer level head-mounted displays (HMDs), Virtual Reality (VR) is progressively entering the consumer market. VR systems provide a new way of experiencing virtual content that is richer than radio, or television, yet also different from how we experience the real world. These unprecedented capabilities for creating new content have the potential to profoundly impact our society. However, little is known about how this new scenario may affect users’ behavior, especially in narrative VR: How does one design or edit 3D scenes effectively in order to retain or guide users’ attention? Can we predict users’ behavior and react accordingly? How does one create a satisfactory cinematic VR cinematic experience? On a more fundamental level, our understanding of how to tell stories may have to be revised for VR. To derive conventions for storytelling from first principles, it is crucial to understand how users explore virtual environments and what constitutes attention. Such an understanding would also inform future designs of user interfaces, eye tracking technology, and other key aspects of VR systems.
Abstract: Understanding and modeling the dynamics of human gaze behavior in 360º environments is a key challenge in computer vision and virtual reality. Generative adversarial approaches could alleviate this challenge by generating a large number of possible scanpaths for unseen images. Existing methods for scanpath generation, however, do not adequately predict realistic scanpaths for 360º images. We present ScanGAN360, a new generative adversarial approach to address this challenging problem. Our network generator is tailored to the specifics of 360º images representing immersive environments. Specifically, we accomplish this by leveraging the use of a spherical adaptation of dynamic-time warping as a loss function and proposing a novel parameterization of 360º scanpaths. The quality of our scanpaths outperforms competing approaches by a large margin and is almost on par with the human baseline. ScanGAN360 thus allows fast simulation of large numbers of virtual observers, whose behavior mimics real users, enabling a better understanding of gaze behavior and novel applications in virtual scene design.
Abstract: Time perception is fluid and affected by manipulations to visual inputs. Previous literature shows that changes to low-level visual properties alter time judgments at the millisecond-level. At longer intervals, in the span of seconds and minutes, high-level cognitive effects (e.g., emotions, memories) elicited by visual inputs affect time perception, but these effects are confounded with semantic information in these inputs, and are therefore challenging to measure and control. In this work, we investigate the effect of asemantic visual properties (pure visual features devoid of emotional or semantic value) on interval time perception. Our experiments were conducted with binary and production tasks in both conventional and head-mounted displays, testing the effects of four different visual features (spatial luminance contrast, temporal frequency, field of view, and visual complexity). Our results reveal a consistent pattern: larger visual changes all shorten perceived time in intervals of up to 3 minutes, remarkably contrary to their effect on millisecond-level perception. Our findings may help alter participants' time perception, which can have broad real-world implications.
Abstract: Virtual reality has the potential to change the way we create and consume content in our everyday life. Entertainment, training, design and manufacturing, communication, or advertising are all applications that already benefit from this new medium reaching consumer level. VR is inherently different from traditional media: it offers a more immersive experience, and has the ability to elicit a sense of presence through the place and plausibility illusions. It also gives the user unprecedented capabilities to explore their environment, in contrast with traditional media. In VR, like in the real world, users integrate the multimodal sensory information they receive to create a unified perception of the virtual world. Therefore, the sensory cues that are available in a virtual environment can be leveraged to enhance the final experience. This may include increasing realism, or the sense of presence; predicting or guiding the attention of the user through the experience; or increasing their performance if the experience involves the completion of certain tasks. In this state-of-the-art report, we survey the body of work addressing multimodality in virtual reality, its role and benefits in the final user experience. The works here reviewed thus encompass several fields of research, including computer graphics, human computer interaction, or psychology and perception. Additionally, we give an overview of different applications that leverage multimodal input in areas such as medicine, training and education, or entertainment; we include works in which the integration of multiple sensory information yields significant improvements, demonstrating how multimodality can play a fundamental role in the way VR systems are designed, and VR experiences created and consumed.
Abstract: Virtual reality (VR) has the potential to change the way people consume content, and has been predicted to become the next big computing paradigm. However, much remains unknown about the grammar and visual language of this new medium, and understanding and predicting how humans behave in virtual environments remains an open problem. In this work, we propose a novel saliency prediction model which exploits the joint potential of spherical convolutions and recurrent neural networks to extract and model the inherent spatio-temporal features from 360º videos. We employ Convolutional Long Short-Term Memory cells (ConvLSTMs) to account for temporal information at the time of feature extraction rather than to post-process spatial features as in previous works. To facilitate spatio-temporal learning, we provide the network with an estimation of the optical flow between 360º frames, since motion is known to be a highly salient feature in dynamic content. Our model is trained with a novel spherical Kullback–Leibler Divergence (KLDiv) loss function specifically tailored for saliency prediction in 360º content. Our approach outperforms previous state-of-the-art works, being able to mimic human visual attention when exploring dynamic 360º videos.
Abstract: Virtual Reality (VR) is gaining popularity in recent years due to the commercialization of personal devices. VR is a new and exciting medium to tell stories, however, the development of Cinematic Virtual Reality (CVR) content is still in an exploratory phase. One of the main reasons is that in this medium the user has now total or partial control of the camera, therefore viewers create their own personal experiences by deciding what to see in every moment, which can potentially hinder the delivery of a pre-established narrative. In the particular case of transitions from one shot to another (movie cuts), viewers may not be aligned with the main elements of the scene placed by the content creator to convey the story. This can result in viewers missing key elements of the narrative. In this work, we explore recent studies that analyze viewers' behavior during cinematic cuts in VR videos, and we discuss guidelines and methods which can help filmmakers with the decision-making process when filming and editing their movies.
Abstract: Lately, the automotive industry has experienced a significant development led by the ambitious objective of creating an autonomous vehicle. This entails understanding driving behaviors in different environments, which usually requires gathering and analyzing large amounts of behavioral data from many drivers. However, this is usually a complex and time-consuming task, and data-driven techniques have proven to be a faster, yet robust alternative to modeling drivers’ behavior. In this work, we propose a deep learning approach to address this challenging problem. We resort to a novel convolutional recurrent architecture to learn spatio-temporal features of driving behaviors based on RGB sequences of the environment in front of the vehicle. Our model is able to predict drivers’ attention in different scenarios while outperforming competing works by a large margin.
Abstract: Virtual reality (VR) is a powerful medium for 360º storytelling, yet content creators are still in the process of developing cinematographic rules for effectively communicating stories in VR. Traditional cinematography has relied for over a century in well-established techniques for editing, and one of the most recurrent resources for this are cinematic cuts that allow content creators to seamlessly transition between scenes. One fundamental assumption of these techniques is that the content creator can control the camera, however, this assumption breaks in VR: users are free to explore the 360º around them. Recent works have studied the effectiveness of different cuts in 360º content, but the effect of directional sound cues while experiencing these cuts has been less explored. In this work, we provide the first systematic analysis of the influence of directional sound cues in users’ behavior across 360º movie cuts, providing insights that can have an impact on deriving conventions for VR storytelling.
Abstract: Virtual Reality (VR) systems increase immersion by reproducing users’ move- ments in the real world. However, several works have shown that this real- to-virtual mapping does not need to be precise in order to convey a realistic experience. Being able to alter this mapping has many potential applications, since achieving an accurate real-to-virtual mapping is not always possible due to limitations in the capture or display hardware, or in the physical space available. In this work, we measure detection thresholds for lateral translation gains of virtual camera motion in response to the corresponding head motion under natural viewing, and in the absence of locomotion, so that virtual camera movement can be either compressed or expanded while these manipulations remain undetected. Finally, we propose three applications for our method, addressing three key problems in VR: improving 6-DoF viewing for captured 360◦ footage, overcoming physical constraints, and reducing simulator sickness. We have further validated our thresholds and evaluated our applications by means of additional user studies confirming that our ma- nipulations remain imperceptible, and showing that (i) compressing virtual camera motion reduces visible artifacts in 6-DoF, hence improving perceived quality, (ii) virtual expansion allows for completion of virtual tasks within a reduced physical space, and (iii) simulator sickness may be alleviated in simple scenarios when our compression method is applied.
Abstract: We report an auditory effect of visual performance degradation in a virtual reality (VR) setting, where the viewing conditions are significantly different from previous studies. With the presentation of temporally congruent but spatially incongruent sound, we can degrade visual performance significantly at detection and recognition levels. We further show that this effect is robust to different types and locations of both auditory and visual stimuli. We also analyze participants behavior with an eye tracker to study the underlying cause of the degradation effect. We find that the performance degradation occurs even in the absence of saccades towards the sound source, during normal gaze behavior. This suggests that this effect is not caused by oculomotor phenomena, but rather by neural interactions or attentional shifts.
Abstract: Virtual Reality (VR) has grown since the first devices for personal use became available on the market. However, the production of cinematographic content in this new medium is still in an early exploratory phase. The main reason is that cinematographic language in VR is still under development, and we still need to learn how to tell stories effectively. A key element in traditional film editing is the use of different cutting techniques, in order to transition seamlessly from one sequence to another. A fundamental aspect of these techniques is the placement and control over the camera. However, VR content creators do not have full control of the camera. Instead, users in VR can freely explore the 360◦ of the scene around them, which potentially leads to very different experiences. While this is desirable in certain applications such as VR games, it may hinder the experience in narrative VR. In this work, we perform a systematic analysis of users’ viewing behavior across cut boundaries while watching professionally edited, narrative 360◦ videos. We extend previous metrics for quantifying user behavior in order to support more complex and realistic footage, and we introduce two new metrics that allow us to measure users’ exploration in a variety of different complex scenarios. From this analysis, (i) we confirm that previous insights derived for simple content hold for professionally edited content, and (ii) we derive new insights that could potentially influence VR content creation, informing creators about the impact of different cuts in the audience’s behavior.
Abstract: We present a convolutional neural network based on panoramic convolutions for saliency prediction in 360º equirectangular panoramas. Our network architecture is designed leveraging recently presented 360º-aware convolutions that represent kernels as patches tangent to the sphere where the panorama is projected, and a spherical loss function that penalizes prediction errors for each pixel depending on its coordinates in a gnomonic projection. Our model is able to successfully predict saliency in 360º scenes from a single image, outperforming other state of-the-art approaches for panoramic content, and yielding more precise results that may help in the understanding of users’ behavior when viewing 360º VR content.
Abstract: We present a method for adding parallax and real-time playback of 360° videos in Virtual Reality headsets. In current video players, the playback does not respond to translational head movement, which reduces the feeling of immersion, and causes motion sickness for some viewers. Given a 360° video and its corresponding depth (provided by current stereo 360 stitching algorithms), a naive image-based rendering approach would use the depth to generate a 3D mesh around the viewer, then translate it appropriately as the viewer moves their head. However, this approach breaks at depth discontinuities, showing visible distortions, while cutting the mesh at such discontinuities leads to ragged silhouettes and holes at disocclusions. We address these issues by improving the given initial depth map to yield cleaner, more natural silhouettes. We rely on a three-layer scene representation, made up of a foreground layer and two static background layers, to handle disocclusions by propagating information from multiple frames for the first background layer, and then inpainting for the second one. Our system works with input from many of today's most popular 360 stereo capture devices (e.g., Yi Halo or GoPro Odyssey), and works well even if the original video does not provide depth information. Our user studies confirm that our method provides a more compelling viewing experience, increasing immersion while reducing discomfort and nausea.
Abstract: With the proliferation of low-cost, consumer level, head-mounted displays (HMDs) we are witnessing a reappearance of virtual reality. However, there are still important stumbling blocks that hinder the achievable visual quality of the results. Knowledge of human perception in virtual environments can help overcome these limitations. In this work, within the much-studied area of perception in virtual environments, we look into the less explored area of crossmodal perception, that is, the interaction of different senses when perceiving the environment. In particular, we look at the influence of sound on visual perception in a virtual reality scenario. First, we assert the existence of a crossmodal visuo-auditory effect in a VR scenario through two experiments, and find that, similar to what has been reported in conventional displays, our visual perception is affected by auditory stimuli in a VR setup. The crossmodal effect in VR is, however, lower than that present in a conventional display counterpart. Having asserted the effect, a third experiment looks at visuo-auditory crossmodality in the context of material appearance perception. We test different rendering qualities, together with the presence of sound, for a series of materials. The goal of the third experiment is twofold: testing whether known interactions in traditional displays hold in VR, and finding insights that can have practical applications in VR content generation (e.g., by reducing rendering costs)
Abstract: Understanding how humans explore virtual environments is crucial for many applications, such as developing compression algorithms or designing effective cinematic virtual reality (VR) content, as well as to develop predictive computational models. We have recorded 780 head and gaze trajectories from 86 users exploring omnidirectional stereo panoramas using VR head-mounted displays. By analyzing the interplay between visual stimuli, head orientation, and gaze direction, we demonstrate patterns and biases of how people explore these panoramas and we present first steps toward predicting time-dependent saliency. To compare how visual attention and saliency in VR are different from conventional viewing conditions, we have also recorded users observing the same scenes in a desktop setup. Based on this data, we show how to adapt existing saliency predictors to VR, so that insights and tools developed for predicting saliency in desktop scenarios may directly transfer to these immersive applications.
Abstract: Traditional cinematography has relied for over a century on a well-established set of editing rules, called continuity editing, to create a sense of situational continuity. Despite massive changes in visual content across cuts, viewers in general experience no trouble perceiving the discontinuous flow of information as a coherent set of events. However, Virtual Reality (VR) movies are intrinsically different from traditional movies in that the viewer controls the camera orientation at all times. As a consequence, common editing techniques that rely on camera orientations, zooms, etc., cannot be used. In this paper we investigate key relevant questions to understand how well traditional movie editing carries over to VR, such as: Does the perception of continuity hold across edit boundaries? Under which conditions? Does viewers’ observational behavior change after the cuts? To do so, we rely on recent cognition studies and the event segmentation theory, which states that our brains segment continuous actions into a series of discrete, meaningful events. We first replicate one of these studies to assess whether the predictions of such theory can be applied to VR. We next gather gaze data from viewers watching VR videos containing different edits with varying parameters, and provide the first systematic analysis of viewers’ behavior and the perception of continuity in VR. From this analysis we make a series of relevant findings; for instance, our data suggests that predictions from the cognitive event segmentation theory are useful guides for VR editing; that different types of edits are equally well understood in terms of continuity; and that spatial misalignments between regions of interest at the edit boundaries favor a more exploratory behavior even after viewers have fixated on a new region of interest. In addition, we propose a number of metrics to describe viewers’ attentional behavior in VR. We believe the insights derived from our work can be useful as guidelines for VR content creation.
Abstract: With the proliferation of low-cost, consumer level, head-mounted displays (HMDs) such as Oculus VR or Sony’s Morpheus, we are witnessing a reappearance of virtual reality. However, there are still important stumbling blocks that hinder the development of applications and reduce the visual quality of the results. Knowledge of human perception in virtual environments can help overcome these limitations. In this paper, within the much-studied area of perception in virtual environments, we chose to look into the less explored area of crossmodal perception, that is, the interaction of different senses when perceiving the environment. In particular, we looked at the influence of sound on visual motion perception in a virtual reality scenario. We first replicated a well-known crossmodal perception experiment, carried out on a conventional 2D display, and then extended it to a 3D headmounted display (HMD). Next, we performed an additional experiment in which we increased the complexity of the stimuli of the previous experiment, to test whether the effects observed would hold in more realistic scenes. We found that the trend which was previously observed in 2D displays is maintained in HMDs, but with an observed reduction of the crossmodal effect. With more complex stimuli the trend holds, and the crossmodal effect is further reduced, possibly due to the presence of additional visual cues.