Abstract Even if the appearance and geometry of the human eye have been extensively studied during the last decade, the geometrical correlation between gaze direction, eyelids aperture and eyelids shape has not been empirically modeled. In this paper, we propose a data‐driven approach for capturing and modeling the subtle features of the human eye region, such as the inner eye corner and the skin bulging effect due to globe orientation. Our approach consists of an original experimental setup to capture the eye region geometry variations combined with a 3D reconstruction method. Regarding the eye region capture, we scanned 55 participants doing 36 eyes poses. To animate a participant's eye region, we register the different poses to a vertex wise correspondence before blending them in a trilinear fashion. We show that our 3D animation results are visually pleasant and realistic while bringing novel eye features compared to state of the art models.

While digital humans are key aspects of the rapidly evolving areas of virtual reality, gaming, and online communications, many applications would benefit from using digital personalized (stylized) representations of users, as they were shown to highly increase immersion, presence and emotional response. In particular, depending on the target application, one may want to look like a dwarf or an elf in a heroic fantasy world, or like an alien on another planet, in accordance with the style of the narrative. While creating such virtual replicas requires stylization of the user’s features onto the virtual character, no formal study has however been conducted to assess the ability to recognize stylized characters. In this paper, we present a perceptual study investigating the effect of the degree of stylization on the ability to recognize an actor, and the subjective acceptability of stylizations. Results show that recognition rates decrease when the degree of stylization increases, while acceptability of the stylization increases. These results provide recommendations to achieve good compromises between stylization and recognition, and pave the way to new stylization methods providing a tradeoff between stylization and recognition of the actor.

In this paper, we present a fully automatic pipeline for generating and stylizing high geometric and textural quality facial rigs. They are automatically rigged with facial blendshapes for animation, and can be used across platforms for applications including virtual reality, augmented reality, remote collaboration, gaming and more. From a set of input facial photos, our approach is to be able to create a photorealistic, fully rigged character in less than seven minutes. The facial mesh reconstruction is based on state-of-the art photogrammetry approaches. Automatic landmarking coupled with ICP registration with regularization provide direct correspondence and registration from a given generic mesh to the acquired facial mesh. Then, using deformation transfer, existing blendshapes are transferred from the generic to the reconstructed facial mesh. The reconstructed face is then fit to the full body generic mesh. Extra geometry such as jaws, teeth and nostrils are retargeted and transferred to the character. An automatic iris color extraction algorithm is performed to colorize a separate eye texture, animated with dynamic UVs. Finally, an extra step applies a style to the photorealis-tic face to enable blending of personalized facial features into any other character. The user's face can then be adapted to any human or non-human generic mesh. A pilot user study was performed to evaluate the utility of our approach. Up to 65% of the participants were successfully able to discern the presence of one's unique facial features when the style was not too far from a humanoid shape.

Current virtual reality systems enable users to explore virtual worlds, fully embodied in avatars. This new type of immersive experience requires specific authoring tools. The traditional ones used in the movie and the video games industries were modified to support immersive visual and audio content. However, few solutions exist to edit haptic content, especially when the whole user's body is involved. To tackle this issue we propose HFX Studio, a haptic editor based on haptic perceptual models. Three models of pressure, vibration and temperature were defined to allow the spatialization of haptic effects on the user's body. These effects can be designed directly on the body (egocentric approach), or specified as objects of the scene (allocentric approach). The perceptual models are also used to describe capabilities of haptic devices. This way the created content is generic, and haptic feedback is rendered on the available devices. The concept has been implemented with the Unity®game engine, a tool already used in VR production. A qualitative pilot user study was conducted to analyze the usability of our tool with expert users. Results shows that the edition of haptic feedback is intuitive for these users.

This paper investigates the role of the embodiment in an immersive video experience. A system allowing to play back omnidirectional videos enhanced with real-time 3D content is presented. It enables the user to be embodied in an avatar and to interact with 3D objects added to the video. A user study was conducted to understand the impact of this embodiment on the user experience. Four different avatars were compared (none, human, ghost and astronaut) as well as three levels of user control on the avatar, and also the possibility to interact with the content. Results show that being embodied has benefits on the user experience but requires the representation to be in line with the story and the avatar to be fully controllable. Indeed a misadapted embodiment may decrease the experience.

Immersive videos allow users to freely explore 4 π steradian scenes within head-mounted displays (HMD), leading to a strong feeling of immersion. However users may miss important elements of the narrative if not facing them. Hence, we propose four visual effects to guide the user's attention. After an informal pilot study, two of the most efficient effects were evaluated through a user study. Results show that our approach has potential but it remains challenging to implicitly drive the user's attention outside of the field of view.

This paper studies the impact of interfaces, allowing nonexpert users to efficiently and intuitively teach a robot to recognize new visual objects. We present challenges that need to be addressed for real-world deployment of robots capable of learning new visual objects in interaction with everyday users. We argue that in addition to robust machine learning and computer vision methods, well-designed interfaces are crucial for learning efficiency. In particular, we argue that interfaces can be key in helping nonexpert users to collect good learning examples and, thus, improve the performance of the overall learning system. Then, we present four alternative human-robot interfaces: Three are based on the use of a mediating artifact (smartphone, wiimote, wiimote and laser), and one is based on natural human gestures (with a Wizard-of-Oz recognition system). These interfaces mainly vary in the kind of feedback provided to the user, allowing him to understand more or less easily what the robot is perceiving and, thus, guide his way of providing training examples differently. We then evaluate the impact of these interfaces, in terms of learning efficiency, usability, and user's experience, through a real world and large-scale user study. In this experiment, we asked participants to teach a robot 12 different new visual objects in the context of a robotic game. This game happens in a home-like environment and was designed to motivate and engage users in an interaction where using the system was meaningful. We then discuss results that show significant differences among interfaces. In particular, we show that interfaces such as the smartphone interface allows nonexpert users to intuitively provide much better training examples to the robot, which is almost as good as expert users who are trained for this task and are aware of the different visual perception and machine learning issues. We also show that artifact-mediated teaching is significantly more efficient for robot learning, and equally good in terms of usability and user's experience, than teaching thanks to a gesture-based human-like interaction.

We introduce a novel way of simulating sensations of motion which does not require an expensive and cumbersome motion platform. Multiple force-feedbacks are applied to the seated user's body to generate a sensation of motion experiencing passive navigation. A set of force-feedback devices such as mobile armrests or headrests are arranged around a seat so that they can apply forces to the user. We have dubbed this new approach HapSeat. A proof of concept has been designed which uses three low-cost force-feedback devices, and two control models have been implemented. Results from the first user study suggest that subjective sensations of motion are reliably generated using either model. Our results pave the way to a novel device to generate consumer motion effects based on our prototype.

In this paper, we present a real world user study of 4 interfaces designed to teach new visual objects to a social robot. This study was designed as a robotic game in order to maintain the user's motivation during the whole experiment. Among the 4 interfaces 3 were based on mediator objects such as an iPhone, a Wiimote and a laser pointer. They also provided the users with different kind of feedback of what the robot is perceiving. The fourth interface was a gesture based interface with a Wizard-of-Oz recognition system added to compare our mediator interfaces with a more natural interaction. Here, we specially studied the impact the interfaces have on the quality of the learning examples and the usability. We showed that providing non-expert users with a feedback of what the robot is perceiving is needed if one is interested in robust interaction. In particular, the iPhone interface allowed non-expert users to provide better learning examples due to its whole visual feedback. Furthermore, we also studied the user's gaming experience and found that in spite of its lower usability, the gestures interface was stated as entertaining as the other interfaces and increases the user's feeling of cooperating with the robot. Thus, we argue that this kind of interface could be well-suited for robotic game.