Immersive Virtual Reality typically requires a head-mounted display (HMD) to visualize the environment and hand-held controllers to interact with the virtual objects. Recently, many applications display full-body avatars to represent the user and animate the arms to follow the controllers. Embodiment is higher when the self-avatar movements align correctly with the user. However, having a full-body self-avatar following the user’s movements can be challenging due to the disparities between the virtual body and the user’s body. This can lead to misalignments in the hand position that can be noticeable when interacting with virtual objects. In this work, we propose five different interaction modes to allow the user to interact with virtual objects despite the self-avatar and controller misalignment and study their influence on embodiment, proprioception, preference, and task performance. We modify aspects such as whether the virtual controllers are rendered, whether controllers are rendered in their real physical location or attached to the user’s hand, and whether stretching the avatar arms to always reach the real controllers. We evaluate the interaction modes both quantitatively (performance metrics) and qualitatively (embodiment, proprioception, and user preference questionnaires). Our results show that the stretching arms solution, which provides body continuity and guarantees that the virtual hands or controllers are in the correct location, offers the best results in embodiment, user preference, proprioception, and performance. Also, rendering the controller does not have an effect on either embodiment or user preference.

An increasing number of virtual reality applications require environments that emulate real-world conditions. These environments often involve dynamic virtual humans showing realistic behaviors. Understanding user perception and navigation among these virtual agents is key for designing realistic and effective environments featuring groups of virtual humans. While collision risk significantly influences human locomotion in the real world, this risk is largely absent in virtual settings. This paper studies the impact of the expected collision feedback on user perception and interaction with virtual crowds. We examine the effectiveness of commonly used collision feedback techniques (auditory cues and tactile vibrations) as well as inducing participants to expect that a physical bump with a real person might occur, as if some virtual humans actually correspond to real persons embodied into them and sharing the same physical space. Our results indicate that the expected collision feedback significantly influences both participant behavior—encompassing global navigation and local movements—and subjective perceptions of presence and copresence. Specifically, the introduction of a perceived risk of actual collision was found to significantly impact global navigation strategies and increase the sense of presence. Auditory cues had a similar effect on global navigation and additionally enhanced the sense of copresence. In contrast, vibrotactile feedback was primarily effective in influencing local movements.

This work deals with the automatic 3D reconstruction of objects from frontal RGB images. This aims at a better understanding of the reconstruction of 3D objects from RGB images and their use in immersive virtual environments. We propose a complete workflow that can be easily adapted to almost any other family of rigid objects. To explain and validate our method, we focus on guitars. First, we detect and segment the guitars present in the image using semantic segmentation methods based on convolutional neural networks. In a second step, we perform the final 3D reconstruction of the guitar by warping the rendered depth maps of a fitted 3D template in 2D image space to match the input silhouette. We validated our method by obtaining guitar reconstructions from real input images and renders of all guitar models available in the ShapeNet database. Numerical results for different object families were obtained by computing standard mesh evaluation metrics such as Intersection over Union, Chamfer Distance, and the F-score. The results of this study show that our method can automatically generate high-quality 3D object reconstructions from frontal images using various segmentation and 3D reconstruction techniques.

This paper deals with automatic 3D reconstruction of objects from frontal RGB images. We propose a complete workflow that can be easily adapted to other object families. First, we detect and segment the object present in the image using Convolutional Neural Networks. In a second step, we perform the final 3D reconstruction of the object by warping the rendered depth maps of a fitted 3D template in 2D image space to match the input silhouette. To explain and validate our method, we focus on 3D guitar reconstructions from real input images and renders of guitar models available in the ShapeNet database. We also show qualitative examples of applying our reconstruction method to other family of objects. The results of this study show that our method can automatically produce high-quality 3D object reconstructions from frontal images using a set of segmentation and 3D reconstruction techniques.

We present methods used to produce and study a first version of an attempt to reconstruct a 1983 live rock concert in virtual reality. An approximately 10 minute performance by the rock band Dire Straits was rendered in virtual reality, based on the use of computer vision techniques to extract the appearance and movements of the band, and crowd simulation for the audience. An online pilot study was conducted where participants experienced the scenario and freely wrote about their experience. The documents produced were analyzed using sentiment analysis, and groups of responses with similar sentiment scores were found and compared. The results showed that some participants were disturbed not by the band performance but by the accompanying virtual audience that surrounded them. The results point to a profound level of plausibility of the experience, though not in the way that the authors expected. The findings add to our understanding of plausibility of virtual environments.

We present an automatic animatable 3D character reconstruction method from frontal and lateral RGB pictures of the person to reconstruct. We propose a method where, after obtaining a 3D reconstruction from a frontal view, we adapt the template of the Skinned Multi-Person Linear Model (SMPL) to it. This frontal modified SMPL is then used in a second step as an input to reconstruct the character from the lateral view, thus obtaining a correct person reconstruction in all dimensions. Our method enables the creation of 3D animatable characters focusing on two main aspects: shape and texture. The obtained results show that the proposed method achieves an accurate automatic 3D character reconstruction from only two views that enhances current methods while offering a reproducible work-flow.

Abstract In this survey we review, classify and compare existing approaches for real‐time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level‐of‐detail (LoD) rendering of animated characters, including polygon‐based, point‐based, and image‐based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo‐instancing, palette skinning, and dynamic key‐pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.

ABSTRACT In this paper, we present a new impostor‐based representation for 3D animated characters supporting real‐time rendering of thousands of agents. We maximize rendering performance by using a collection of pre‐computed impostors sampled from a discrete set of view directions. Our approach differs from previous work on view‐dependent impostors in that we use per‐joint rather than per‐character impostors. Our characters are animated by applying the joint rotations directly to the impostors, instead of choosing a single impostor for the whole character from a set of pre‐defined poses. This offers more flexibility in terms of animation clips, as our representation supports any arbitrary pose, and thus, the agent behavior is not constrained to a small collection of pre‐defined clips. Because our impostors are intended to be valid for any pose, a key issue is to define a proper boundary for each impostor to minimize image artifacts while animating the agents. We pose this problem as a variational optimization problem and provide an efficient algorithm for computing a discrete solution as a pre‐process. To the best of our knowledge, this is the first time a crowd rendering algorithm encompassing image‐based performance, small graphics processing unit footprint, and animation independence is proposed. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract Rendering detailed animated characters is a major limiting factor in crowd simulation. In this paper we present a new representation for 3D animated characters which supports output‐sensitive rendering. Our approach is flexible in the sense that it does not require us to pre‐define the animation sequences beforehand, nor to pre‐compute a dense set of pre‐rendered views for each animation frame. Each character is encoded through a small collection of textured boxes storing colour and depth values. At runtime, each box is animated according to the rigid transformation of its associated bone and a fragment shader is used to recover the original geometry using a dual‐depth version of relief mapping. Unlike competing output‐sensitive approaches, our compact representation is able to recover high‐frequency surface details and reproduces view‐motion parallax effectively. Our approach drastically reduces both the number of primitives being drawn and the number of bones influencing each primitive, at the expense of a very slight per‐fragment overhead. We show that, beyond a certain distance threshold, our compact representation is much faster to render than traditional level‐of‐detail triangle meshes. Our user study demonstrates that replacing polygonal geometry by our impostors produces negligible visual artefacts.