Errors that arise due to a mismatch in the dynamics of a person’s motion and the visualized movements of their avatar in virtual reality are termed “physicality errors” to distinguish them from simple physical errors, such as footskate. Physicality errors involve plausible motions, but with dynamic inconsistencies. Even with perfect tracking and ideal virtual worlds, such errors are inevitable in virtual reality whenever a person adopts an avatar that does not match their own proportions or lifts a virtual object that appears heavier than the movement of their hand. This study investigates people’s sensitivity to physicality errors to understand when they are likely to be noticeable and need to be mitigated. It uses a simple, well-understood exercise of a dumbbell lift to explore the impact of motion kinematics and varied sources of visual information, including changing body size, changing the size of manipulated objects, and displaying muscular strain. Results suggest that kinematic (motion) information has a dominant impact on perception of effort, but visual information, particularly the visual size of the lifted object, has a strong impact on perceived weight. This can lead to perceptual mismatches that reduce perceived naturalness. Small errors may not be noticeable, but large errors reduce naturalness. Further results are discussed, which inform the requirements for animation algorithms.

In this paper, we present a novel real-time physical interaction system that allows users to throw a virtual ball without using an intermediary device such as a controller. A dataset of throwing motions was captured from six actors, from which ground truth Point of Release (PoR) frames were calculated. We trained a PoR prediction model using motion features extracted from arm joints, and developed a detection algorithm to predict the PoR of a throwing motion in real-time. Evaluation of the system with pre-recorded throwing motion data results in detection errors of less than 50 ms. Qualitative results from six users of the real-time system in VR indicate that the task of throwing without a controller was very natural, although the system performed better for underarm than overarm throws. Finally, we report the relative importance of different joints and motion features for the PoR prediction task.

Our aim is to develop a better understanding of how the Point of Release (PoR) of a ball affects the perception of animated throwing motions. We present the results of a perceptual study where participants viewed animations of a virtual human throwing a ball, in which the point of release was modified to be early or late. We found that errors in overarm throws with a late PoR are detected more easily than an early PoR, while the opposite is true for underarm throws. The viewpoint and the distance the ball travels also have an effect on perceived realism. The results of this research can help improve the plausibility of throwing animations in interactive applications such as games or VR.

Unlike their human counterparts, artificial agents such as robots and game characters may be deployed with a large variety of face and body configurations. Some have articulated bodies but lack facial features, and others may be talking heads ending at the neck. Generally, they have many fewer degrees of freedom than humans through which they must express themselves, and there will inevitably be a filtering effect when mapping human motion onto the agent. In this article, we investigate filtering effects on three types of embodiments: (a) an agent with a body but no facial features, (b) an agent with a head only, and (c) an agent with a body and a face. We performed a full performance capture of a mime actor enacting short interactions varying the non-verbal expression along five dimensions (e.g., level of frustration and level of certainty) for each of the three embodiments. We performed a crowd-sourced evaluation experiment comparing the video of the actor to the video of an animated robot for the different embodiments and dimensions. Our findings suggest that the face is especially important to pinpoint emotional reactions but is also most volatile to filtering effects. The body motion, on the other hand, had more diverse interpretations but tended to preserve the interpretation after mapping and thus proved to be more resilient to filtering.

We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose the first approach based on finding semantic information represented by latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process ( SV-DHDP) model. The fidelity of the patterns is computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise.

No abstract available.

We present an algorithmic framework for the early classification of human intentions, and use it to accurately predict future human motions when planning the path of a robot in an environment that is shared with humans. During an off-line learning phase, a classifier that can recognize when a human intends to interact with the robot is trained. At runtime, this trained classifier allows us to recognize humans who intend to interact with, or obstruct, the robot in some way. We validate our approach using both recorded and simulated data in an environment in which some humans intentionally obstruct the robot. Our classifier identifies these potential blockers, thus allowing the robot to safely and efficiently navigate the environment by minimizing the chances of being blocked.

Passive optical motion capture is one of the predominant technologies for capturing high fidelity human skeletal motion, and is a workhorse in a large number of areas such as bio-mechanics, film and video games. While most state-of-the-art systems can automatically identify and track markers on the larger parts of the human body, the markers attached to fingers provide unique challenges and usually require extensive manual cleanup. In this work we present a robust online method for identification and tracking of passive motion capture markers attached to the fingers of the hands. The method is especially suited for large capture volumes and sparse marker sets of 3 to 10 markers per hand. Once trained, our system can automatically initialize and track the markers, and the subject may exit and enter the capture volume at will. By using multiple assignment hypotheses and soft decisions, it can robustly recover from a difficult situation with many simultaneous occlusions and false observations (ghost markers). We evaluate the method on a collection of sparse marker sets commonly used in industry and in the research community. We also compare the results with two of the most widely used motion capture platforms: Motion Analysis Cortex and Vicon Blade. The results show that our method is better at attaining correct marker labels and is especially beneficial for real-time applications.

Crowd simulation has been an active and important area of research in the field of interactive 3D graphics for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose a new approach based on finding latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd's behaviour. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is then computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly.

Abstract Communicative behaviors are a very important aspect of human behavior and deserve special attention when simulating groups and crowds of virtual pedestrians. Previous approaches have tended to focus on generating believable gestures for individual characters and talker‐listener behaviors for static groups. In this paper, we consider the problem of creating rich and varied conversational behaviors for data‐driven animation of walking and jogging characters. We captured ground truth data of participants conversing in pairs while walking and jogging. Our stylized splicing method takes as input a motion captured standing gesture performance and a set of looped full body locomotion clips. Guided by the ground truth metrics, we perform stylized splicing and synchronization of gesture with locomotion to produce natural conversations of characters in motion. Copyright © 2016 John Wiley & Sons, Ltd.

It is common practice in movies and games to use different actors for the voice and body/face motion of a virtual character. What effect does the combination of these different modalities have on the perception of the viewer? In this article, we conduct a series of experiments to evaluate the distinctiveness and attractiveness of human motions (face and body) and voices. We also create combination characters called FrankenFolks, where we mix and match the voice, body motion, face motion, and avatar of different actors and ask which modality is most dominant when determining distinctiveness and attractiveness or whether the effects are cumulative.

Physical simulation and rendering of cloth is widely used in 3D graphics applications to create realistic and compelling scenes. However, cloth animation can be slow to compute and difficult to specify. In this paper, we present a set of experiments in which we explore some factors that contribute to the perception of cloth, to determine how efficiency could be improved without sacrificing realism. Using real video footage of several fabrics covering a wide range of visual appearances and dynamic behaviors, and their simulated counterparts, we explore the interplay of visual appearance and dynamics in cloth animation.

A better understanding of how intentions and traits are perceived from body movements is required for the design of more effective virtual characters that behave in a socially realistic manner. For this purpose, realistic body motion, captured from human movements, is being used more frequently for creating characters with natural animations in games and entertainment. However, it is not always clear for programmers and designers which specific motion parameters best convey specific information such as certain emotions, intentions, or traits. We conducted two experiments to investigate whether the perceived traits of actors could be determined from their body motion, and whether these traits were associated with their perceived intentions. We first recorded body motions from 26 professional actors, who were instructed to move in a “hero”-like or a “villain”-like manner. In the first experiment, 190 participants viewed individual video recordings of these actors and were required to provide ratings to the body motion stimuli along a series of different cognitive dimensions (intentions, attractiveness, dominance, trustworthiness, and distinctiveness). The intersubject ratings across observers were highly consistent, suggesting that social traits are readily determined from body motion. Moreover, correlational analyses between these ratings revealed consistent associations across traits, for example, that perceived “good” intentions were associated with higher ratings of attractiveness and dominance. Experiment 2 was designed to elucidate the qualitative body motion cues that were critical for determining specific intentions and traits from the hero- and villain-like body movements. The results revealed distinct body motions that were readily associated with the perception of either “good” or “bad” intentions. Moreover, regression analyses revealed that these ratings accurately predicted the perception of the portrayed character type. These findings indicate that intentions and social traits are communicated effectively via specific sets of body motion features. Furthermore, these results have important implications for the design of the motion of virtual characters to convey desired social information.

It is known that humans can be insensitive to large changes in illumination. For example, if an object of interest is extracted from one digital photograph and inserted into another, we do not always notice the differences in illumination between the object and its new background. This inability to spot illumination inconsistencies is often the key to success in digital “doctoring” operations. We present a set of experiments in which we explore the perception of illumination in outdoor scenes. Our results can be used to predict when and why inconsistencies go unnoticed. Applications of the knowledge gained from our studies include smarter digital “cut-and-paste” and digital “fake” detection tools, and image-based composite scene backgrounds for layout and previsualization.

Biped controller design pursues two fundamental goals; simulated walking should look human-like and robust against perturbation while maintaining its balance. Normal gait is a pattern of walking that humans normally adopt in undisturbed situations. It has previously been postulated that normal gait is more energy efficient than abnormal or impaired gaits. However, it is not clear whether normal gait is also superior to abnormal gait patterns with respect to other factors, such as stability. Understanding the correlation between gait and stability is an important aspect of biped controller design. We studied this issue in two sets of experiments with human participants and a simulated biped. The experiments evaluated the degree of resilience to external pushes for various gait patterns. We identified four gait factors that affect the balance-recovery capabilities of both human and simulated walking. We found that crouch gait is significantly more stable than normal gait against lateral push. Walking speed and the timing/magnitude of disturbance also affect gait stability. Our work would provide a potential way to compare the performance of biped controllers by normalizing their output gaits and improve their performance by adjusting these decisive factors.

A pervasive artifact that occurs when visualizing 3D content is the so-called "cardboarding" effect, where objects appear flat due to depth compression, with relatively little research conducted to perceptually quantify its effects. Our aim is to shed light on the subjective preferences and practical perceptual limits of stereo vision with respect to cardboarding. We present three experiments that explore the consequences of displaying simple scenes with reduced depths using both subjective ratings and adjustments and objective sensitivity metrics. Our results suggest that compressing depth to 80% or above is likely to be acceptable, whereas sensitivity to the cardboarding artifact below 30% is very high. These values could be used in practice as guidelines for commonplace depth mapping operations in 3D production pipelines.

Animation budget constraints during the development of a game often call for the use of a limited set of generic motions. Editing operations are thus generally required to animate virtual characters with a sufficient level of variety. Evaluating the perceptual plausibility of edited animations can therefore contribute greatly towards producing visually plausible animations. In this article, we study observers’ sensitivity to manipulations of overarm and underarm biological throwing animations. In the first experiment, we modified the release velocity of the ball while leaving the motion of the virtual thrower and the angle of release of the ball unchanged. In the second experiment, we evaluated the possibility of further modifying throwing animations by simultaneously editing the motion of the thrower and the release velocity of the ball, using dynamic time warping. In both experiments, we found that participants perceived shortened underarm throws to be particularly unnatural. We also found that modifying the thrower's motion in addition to modifying the release velocity of the ball does not significantly improve the perceptual plausibility of edited throwing animations. In the third experiment, we modified the angle of release of the ball while leaving the magnitude of release velocity and the motion of the thrower unchanged, and found that this editing operation is efficient for improving the perceptual plausibility of shortened underarm throws. Finally, in Experiment 4, we replaced the virtual human thrower with a mechanical throwing device (a ramp) and found the opposite pattern of sensitivity to modifications of the release velocity, indicating that biological and physical throws are subject to different perceptual rules. Our results provide valuable guidelines for developers of games and virtual reality applications by specifying thresholds for the perceptual plausibility of throwing manipulations while also providing several interesting insights for researchers in visual perception of biological motion.

In many application areas, such as animation for pre-visualizing movie sequences and choreography for dance or other types of performance, only a high-level description of the desired scene is provided as input, either written or verbal. Such sparsity, however, lends itself well to the creative process, as the choreographer, animator or director can be given more choice and control of the final scene. Animating scenes with multi-character interactions can be a particularly complex process, as there are many different constraints to enforce and actions to synchronize. Our novel 'generate-and-rank' approach rapidly and semi-automatically generates data-driven multi-character interaction scenes from high-level graphical descriptions composed of simple clauses and phrases. From a database of captured motions, we generate a multitude of plausible candidate scenes. We then efficiently and intelligently rank these scenes in order to recommend a small but high-quality and diverse selection to the user. This set can then be refined by re-ranking or by generating alternatives to specific interactions. While our approach is applicable to any scenes that depict multi-character interactions, we demonstrate its efficacy for choreographing fighting scenes and evaluate it in terms of performance and the diversity and coverage of the results.

Better understanding of how social traits and intentions are conveyed through human body motion is central in creating believable, life-like virtual characters. Body motion conveys socially relevant information on which people make judgments, such as other people's personality or gender. [e.g. Koppensteiner and Grammer 2011, Thoresen et al. 2012]. However, it is unclear how the body motion features are related to the perceived social traits and intentions of others. The main goal of this study is to examine how visual information from human body motion is related to social traits and intention perception. Furthermore, we also investigated if body motion features can be used as predictors of social intentions.

Variations in the timing and speed of movements in human interactions carry important social information. For example, seeing one player push another player at a football game, we can deduce whether the player being pushed resisted or anticipated the oncoming push based on subtle differences in the timing and velocity of movements of both players. With the development of computer animations of human characters, it is important to understand the sensitivity and limits of human perception in such interactions to accurately portray human interactions.

Recent advances in rendering and data-driven animation have enabled the creation of compelling characters with impressive levels of realism. While data-driven techniques can produce animations that are extremely faithful to the original motion, many challenging problems remain because of the high complexity of human motion. A better understanding of the factors that make human motion recognizable and appealing would be of great value in industries where creating a variety of appealing virtual characters with realistic motion is required. To investigate these issues, we captured thirty actors walking, jogging and dancing, and applied their motions to the same virtual character (one each for the males and females). We then conducted a series of perceptual experiments to explore the distinctiveness and attractiveness of these human motions, and whether characteristic motion features transfer across an individual's different gaits. Average faces are perceived to be less distinctive but more attractive, so we explored whether this was also true for body motion. We found that dancing motions were most easily recognized and that distinctiveness in one gait does not predict how recognizable the same actor is when performing a different motion. As hypothesized, average motions were always amongst the least distinctive and most attractive. Furthermore, as 50% of participants in the experiment were Caucasian European and 50% were Asian Korean, we found that the latter were as good as or better at recognizing the motions of the Caucasian actors than their European counterparts, in particular for dancing males, whom they also rated more highly for attractiveness.

Understanding whether observers are sensitive to physical distortions in dynamic events is important in order to develop plausible simulations while saving time on details that observers cannot perceive [Barzel et al. 1996]. In this experiment, we evaluated observers' sensitivity to physical distortions in virtual throwing animations. As previous results showed a greater sensitivity to physical distortions on virtual human characters rather than on simple objects [Reitsma et al. 2008], we studied the effect of using both a biological human throw and a simple physical simulation (free fall of a ball on a ramp). The results show that observers' sensitivity to errors depends on the animacy of the thrower.

Subtle animation details such as finger or facial movements help to bring virtual characters to life and increase their appeal. However, it is not always possible to capture finger animations simultaneously with full-body motion, due to limitations of the setup or tight production schedules. Therefore, hand motions are often either omitted, manually created by animators, or captured during a separate session and spliced with full body animation. In this paper, we investigate the perceived fidelity of hand animations where all the degrees of freedom of the hands are computed from reduced marker sets. In a set of perceptual experiments, we found that finger motions reconstructed with inverse kinematics from a reduced marker set of eight markers per hand are perceived to be very similar to the corresponding motions reconstructed using a full set of twenty markers. We demonstrate how using this reduced set of eight large markers enabled us to capture the finger and full-body motions of two actors performing a range of relatively unconstrained actions using a 13-camera motion capture system. This serves to simplify the capture process and to significantly reduce the time for cleanup, while preserving the natural biological movements of the hands relative to the actions performed.

Abstract Rendering realistic illumination effects for complex animated scenes with many dynamic objects or characters is computationally expensive. Yet, it is not obvious how important such accurate lighting is for the overall perceived realism in these scenes. In this paper, we present a methodology to evaluate the perceived fidelity of illumination in scenes with dynamic aggregates, such as crowds, and explore several factors which may affect this perception. We focus in particular on evaluating how a popular spherical harmonics lighting method can be used to approximate realistic lighting of crowds. We conduct a series of psychophysical experiments to explore how a simple approach to approximating global illumination, using interpolation in the temporal domain, affects the perceived fidelity of dynamic scenes with high geometric, motion, and illumination complexity. We show that the complexity of the geometry and temporal properties of the crowd entities, the motion of the aggregate as a whole, the type of interpolation (i.e., of the direct and/or indirect illumination coefficients), and the presence or absence of colour all affect perceived fidelity. We show that high (i.e., above 75%) levels of perceived scene fidelity can be maintained while interpolating indirect illumination for intervals of up to 30 frames, resulting in a greater than three‐fold rendering speed‐up.

ABSTRACT Recent progress in real‐time simulations has led to a higher demand for believability from virtual characters. Background characters are becoming a more integral part of games, with emphasis being placed in particular on interactions between them. Conversing groups can play a significant role in adding plausibility, or a sense of presence, to a real‐time simulation. However, it is not obvious how best to generate and vary these kinds of groups. In this paper, using anthropological standards for interacting distances and formations, we conduct a series of experiments to examine how these parameters inherent in human conversation are perceived for virtual characters. Our results show that, although participants were sensitive to both distance and orientation changes between talkers and listeners in a virtual conversation, they were not as sensitive to anomalous gesturing behaviours across different distances. Copyright © 2012 John Wiley & Sons, Ltd.

With recent advances in real-time graphics technology, more realistic, believable and appealing virtual characters are needed than ever before. Both player-controlled avatars and non-player characters are now starting to interact with the environment, other virtual humans and crowds. However, simulating physical contacts between characters and matching appropriate reactions to specific actions is a highly complex problem, and timing errors, force mismatches and angular distortions are common. To investigate the effect of such anomalies on the perceived realism of two-character interactions, we captured a motion corpus of pushing animations and corresponding reactions and then conducted a series of perceptual experiments. We found that participants could easily distinguish between five different interaction forces, even when only one of the characters was visible. Furthermore, they were sensitive to all three types of anomalous interactions: timing errors of over 150ms were acceptable less than 50% of the time, with early or late reactions being equally perceptible; participants could perceive force mismatches, though over-reactions were more acceptable than under-reactions; finally, angular distortions when a character reacts to a pushing force reduce the acceptability of the interactions, but there is some evidence for a preference of expansion away from the pushing character's body. Our results provide insights to aid in designing motion capture sessions, motion editing strategies and balancing animation budgets.

In this article, we evaluate the effects of position, orientation, and camera viewpoint on the plausibility of pedestrian formations. In a set of three perceptual studies, we investigated how humans perceive characteristics of virtual crowds in static scenes reconstructed from annotated still images, where the orientations and positions of the individuals have been modified. We found that by applying rules based on the contextual information of the scene, we improved the perceived realism of the crowd formations when compared to random formations. We also examined the effect of camera viewpoint on the plausibility of virtual pedestrian scenes, and we found that an eye-level viewpoint is more effective for disguising random behaviors, while a canonical viewpoint results in these behaviors being perceived as less realistic than an isometric or top-down viewpoint. Results from these studies can help in the creation of virtual crowds, such as computer graphics pedestrian models or architectural scenes, and identify situations when users' perception is less accurate.

Architecture is design in spatial context. The only current methods for representing context involve designing in a heavyweight computer-aided design system, using a full model of existing buildings and landscape, or sketching on a panoramic photo. The former is too cumbersome; the latter is too restrictive in viewpoint and in the handling of occlusions and topography. We introduce a novel approach to presenting context such that it is an integral component in a lightweight conceptual design system. We represent sites through a fusion of data available from different sources. We derive a site model from geographic elevation data, on-site point-to-point distance measurements, and images of the site. To acquire and process the data, we use publicly available data sources, multidimensional scaling techniques and refinements of recent bundle adjustment techniques. We offer a suite of interactive tools to acquire, process, and combine the data into a lightweight stroke and image-billboard representation. We create multiple and linked pop-ups derived from images, forming a lightweight representation of a three-dimensional environment. We implemented our techniques in a stroke-based conceptual design system we call Insitu . We developed our work through continuous interaction with professional designers. We present designs created with our new techniques integrated in a conceptual design system.

No abstract available.

No abstract available.

Crowds and groups are a vital element of life, and simulating them in a convincing manner is one of the great challenges in computer graphics and interactive techniques. This course focuses on the problem of efficiently simulating realistic crowd and group behavior for a range of applications, including games and design of spaces. It covers data driven methods, where the characteristics of crowds are simulated based on real world data; evaluation and perceptual issues, and creation of behavioral variety; interactive simulation and control of large scale crowds and traffic for games and other real time applications; and finally a case study of using crowd simulation for design of spaces in the Disney theme parks.

Understanding the perception of humanoid character motion can provide insights that will enable realism, accuracy, computational cost and data storage space to be optimally balanced. In this sketch we describe a preliminary perceptual evaluation of human motion timewarping, a common editing method for motion capture data. During the experiment, participants were shown pairs of walking motion clips, both timewarped and at their original speed, and asked to identify the real animation. We found a statistically significant difference between speeding up and slowing down, which shows that displaying clips at higher speeds produces obvious artifacts, whereas even significant reductions in speed were perceptually acceptable.

Abstract Skinning is a simple yet popular deformation technique combining compact storage with efficient hardware accelerated rendering. While skinned meshes (such as virtual characters) are traditionally created by artists, previous work proposes algorithms to construct skinning automatically from a given vertex animation. However, these methods typically perform well only for a certain class of input sequences and often require long pre‐processing times. We present an algorithm based on iterative coordinate descent optimization which handles arbitrary animations and produces more accurate approximations than previous techniques, while using only standard linear skinning without any modifications or extensions. To overcome the computational complexity associated with the iterative optimization, we work in a suitable linear subspace (obtained by quick approximate dimensionality reduction) and take advantage of the typically very sparse vertex weights. As a result, our method requires about one or two orders of magnitude less pre‐processing time than previous methods.

Abstract Modal analysis techniques are often used to animate deformable objects in real time. In order to achieve performance improvements, the degrees of freedom of the problem may be reduced by using the main global deformations alone. However, this can lead to a reduction in quality and realism due to the lack of local behaviors. To solve this problem, we present a new method to add local deformations to modal analysis simulations. We perceptually evaluate our method with a set of experiments, thereby deriving guidelines for when and how local deformations can best be used. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Fuzzy controllers are a popular method for animated character perception and control. A drawback of fuzzy systems is that time intensive manual calibration of system parameters is required. We introduce a new Genetic‐Fuzzy System (GFS) that automates the tuning process of rules for animated character steering. An advantage of our GFS is that it is able to adapt the rules for steering behaviour during run time. We explore the parameter space of the new GFS, and discuss how the GFS can be implemented to run as a background process during normal execution of a simulation. Copyright © 2010 John Wiley & Sons, Ltd.

Virtual characters are much in demand for animated movies, games, and other applications. Rapid advances in performance capture and advanced rendering techniques have allowed the movie industry in particular to create characters that appear very human-like. However, with these new capabilities has come the realization that such characters are yet not quite “right.” One possible hypothesis is that these virtual humans fall into an “Uncanny Valley”, where the viewer's emotional response is repulsion or rejection, rather than the empathy or emotional engagement that their creators had hoped for. To explore these issues, we created three animated vignettes of an arguing couple with detailed motion for the face, eyes, hair, and body. In a set of perceptual experiments, we explore the relative importance of different anomalies using two different methods: a questionnaire to determine the emotional response to the full-length vignettes, with and without facial motion and audio; and a 2AFC (two alternative forced choice) task to compare the performance of a virtual “actor” in short clips (extracts from the vignettes) depicting a range of different facial and body anomalies. We found that the facial anomalies are particularly salient, even when very significant body animation anomalies are present.

In many scenes with human characters, interacting groups are an important factor for maintaining a sense of realism. However, little is known about what makes these characters appear realistic. In this paper, we investigate human sensitivity to audio mismatches (i.e., when individuals' voices are not matched to their gestures) and visual desynchronization (i.e., when the body motions of the individuals in a group are mis-aligned in time) in virtual human conversers. Using motion capture data from a range of both polite conversations and arguments, we conduct a series of perceptual experiments and determine some factors that contribute to the plausibility of virtual conversing groups. We found that participants are more sensitive to visual desynchronization of body motions, than to mismatches between the characters' gestures and their voices. Furthermore, synthetic conversations can appear sufficiently realistic once there is an appropriate balance between talker and listener roles. This is regardless of body motion desynchronization or mismatched audio.

Linear blending is a very popular skinning technique for virtual characters, even though it does not always generate realistic deformations. Recently, nonlinear blending techniques (such as dual quaternions) have been proposed in order to improve upon the deformation quality of linear skinning. The trade-off consists of the increased vertex deformation time and the necessity to redesign parts of the 3D engine. In this paper, we demonstrate that any nonlinear skinning technique can be approximated to an arbitrary degree of accuracy by linear skinning, using just a few samples of the nonlinear blending function (virtual bones). We propose an algorithm to compute this linear approximation in an automatic fashion, requiring little or no interaction with the user. This enables us to retain linear skinning at the core of our 3D engine without compromising the visual quality or character setup costs.

Abstract Films like Shrek, Madagascar, The Chronicles of Narnia and Charlotte's web all have something in common: realistic quadruped animations. While the animation of animals has been popular for a long time, the technical challenges associated with creating highly realistic, computer generated creatures have been receiving increasing attention recently. The entertainment, education and medical industries have increased the demand for simulation of realistic animals in the computer graphics area. In order to achieve this, several challenges need to be overcome: gathering and processing data that embodies the natural motion of an animal – which is made more difficult by the fact that most animals cannot be easily motion‐captured; building accurate kinematic models for animals, with adapted animation skeletons in particular; and developing either kinematic or physically‐based animation methods, either by embedding some a priori knowledge about the way that quadrupeds locomote and/or adopting examples of real motion. In this paper, we present an overview of the common techniques used to date for realistic quadruped animation. This includes an outline of the various ways that realistic quadruped motion can be achieved, through video‐based acquisition, physics based models, inverse kinematics or some combination of the above.

No abstract available.

In this article, we investigate human sensitivity to the coordination and timing of conversational body language for virtual characters. First, we captured the full body motions (excluding faces and hands) of three actors conversing about a range of topics, in either a polite (i.e., one person talking at a time) or debate/argument style. Stimuli were then created by applying the motion-captured conversations from the actors to virtual characters. In a 2AFC experiment, participants viewed paired sequences of synchronized and desynchronized conversations and were asked to guess which was the real one. Detection performance was above chance for both conversation styles but more so for the polite conversations, where desynchronization was more noticeable.

In this paper, our aim is to determine factors that influence the perceived sex of virtual characters. In Experiment 1, four different model types were used: highly realistic male and female models, an androgynous character, and a point light walker. Three different types of motion were applied to all models: motion captured male and female walks, and neutral synthetic walks. We found that both form and motion influence sex perception for these characters: for neutral synthetic motions, form determines perceived sex, whereas natural motion affects the perceived sex of both androgynous and realistic forms. These results indicate that the use of neutral walks is better than creating ambiguity by assigning an incongruent motion. In Experiment 2 we investigated further the influence of body shape and motion on realistic male and female models and found that adding stereotypical indicators of sex to the body shapes influenced sex perception. Also, that exaggerated female body shapes influences sex judgements more than exaggerated male shapes. These results have implications for variety and realism when simulating large crowds of virtual characters.

In order to analyze the emotional content of motions portrayed by different characters, we created real and virtual replicas of an actor exhibiting six basic emotions: sadness, happiness, surprise, fear, anger, and disgust. In addition to the video of the real actor, his actions were applied to five virtual body shapes: a low- and high-resolution virtual counterpart, a cartoon-like character, a wooden mannequin, and a zombie-like character (Figures 1 and 2). In a point light condition, we also tested whether the absence of a body affected the perceived emotion of the movements. Participants were asked to rate the actions based on a list of 41 more complex emotions. We found that the perception of emotional actions is highly robust and to the most part independent of the character's body, so long as form is present. When motion alone is present, emotions were generally perceived as less intense than in the cases where form was present.

We present a novel perceptual method to reduce the visual redundancy of unstructured lumigraphs, an image based representation designed for interactive rendering. We combine features of the unstructured lumigraph algorithm and image fidelity metrics to efficiently rank the perceptual impact of the removal of subregions of input views ( subviews ). We use a greedy approach to estimate the order in which subviews should be pruned to minimize perceptual degradation at each step. Renderings using varying numbers of subviews can then be easily visualized with confidence that the retained subviews are well chosen, thus facilitating the choice of how many to retain. The regions of the input views that are left are repacked into a texture atlas. Our method takes advantage of any scene geometry information available but only requires a very coarse approximation. We perform a user study to validate its behaviour, as well as investigate the impact of the choice of image fidelity metric as well as that of user parameters. The three metrics considered fall in the physical, statistical and perceptual categories. The overall benefit of our method is the semiautomation of the view selection process, resulting in unstructured lumigraphs that are thriftier in texture memory use and faster to render. Using the same framework, we adjust the parameters of the unstructured lumigraph algorithm to optimise it on a scene by scene basis.

Panoramic images are very effective at conveying a visual sense of presence at very low cost and great ease of authoring. They are, however, limited in the navigation options they offer, unlike 3D representations. It is therefore desirable to provide pleasing transitions from one panorama to another, or from a panorama to a 3D model. We focus on motions where the viewers move toward an area of interest, and on the problem of dealing with occluders in their path. We discuss existing transition approaches, with emphasis on the additional information they require and on the constraints they place on the authoring process. We propose a compromise approach based on faking the parallax effect with occluder mattes. We conduct a user study to determine whether additional information does in fact increase the visual appeal of transitions. We observe that the creation of occluder mattes alone is only justified if the fake parallax effect can be synchronized with the camera motion (but not necessarily consistent with it), and if viewpoint discrepancies at occlusion boundaries are small. The faster the transition, the less perceptual value there is in creating mattes. Information on view alignment is always useful, as a dissolve effect is always preferred to fading to black and back.

A deeper understanding of what makes animation perceptually plausible would benefit a number of applications, such as approximate collision detection and goal-directed animation. In a series of psychophysical experiments, we examine how measurements of perceptual sensitivity in realistic physical simulations compare to similar measurements done in more abstract settings. We find that participant tolerance for certain types of errors is significantly higher in a realistic snooker scenario than in the abstract test settings previously used to examine those errors. By contrast, we find tolerance for errors displayed in realistic but more neutral environments was not different from tolerance for those errors in abstract settings. Additionally, we examine the interaction of auditory and visual cues in determining participant sensitivity to spatiotemporal errors in rigid body collisions. We find that participants are predominantly affected by visual cues. Finally, we find that tolerance for spatial gaps during collision events is constant for a wide range of viewing angles if the effect of foreshortening and occlusion caused by the viewing angle is taken into account.

Populated virtual environments need to be simulated with as much variety as possible. By identifying the most salient parts of the scene and characters, available resources can be concentrated where they are needed most. In this paper, we investigate which body parts of virtual characters are most looked at in scenes containing duplicate characters or clones . Using an eye-tracking device, we recorded fixations on body parts while participants were asked to indicate whether clones were present or not. We found that the head and upper torso attract the majority of first fixations in a scene and are attended to most. This is true regardless of the orientation, presence or absence of motion, sex, age, size, and clothing style of the character. We developed a selective variation method to exploit this knowledge and perceptually validated our method. We found that selective colour variation is as effective at generating the illusion of variety as full colour variation. We then evaluated the effectiveness of four variation methods that varied only salient parts of the characters. We found that head accessories, top texture and face texture variation are all equally effective at creating variety, whereas facial geometry alterations are less so. Performance implications and guidelines are presented.

Various methods have been proposed to animate and render large crowds of humans in real time for applications such as games and interactive walkthroughs. Recent methods have been developed to render large numbers of pre-computed image-based human representations (Impostors) by exploiting commodity graphics hardware, thus achieving very high frame-rates while maintaining visual fidelity. Unfortunately, these images consume a lot of texture memory, no in-betweening is possible, and the variety of animations that can be shown is severely restricted. This paper proposes an alternative method that significantly improves upon pre-computed impostors: automatically generated 2D polygonal characters (or Polypostors). When compared with image-based crowd rendering systems, Polypostors exhibit a similarly high level of rendering efficiency and visual fidelity, with considerably lower memory requirements (up to a factor of 30 in our test cases). Furthermore, Polypostors enable simple in-betweening and can thus deliver a greater variety of animations at any required level of smoothness with almost no overhead.

Skinning of skeletally deformable models is extensively used for real-time animation of characters, creatures and similar objects. The standard solution, linear blend skinning, has some serious drawbacks that require artist intervention. Therefore, a number of alternatives have been proposed in recent years. All of them successfully combat some of the artifacts, but none challenge the simplicity and efficiency of linear blend skinning. As a result, linear blend skinning is still the number one choice for the majority of developers. In this article, we present a novel skinning algorithm based on linear combination of dual quaternions. Even though our proposed method is approximate, it does not exhibit any of the artifacts inherent in previous methods and still permits an efficient GPU implementation. Upgrading an existing animation system from linear to dual quaternion skinning is very easy and has a relatively minor impact on runtime performance.

When simulating large crowds, it is inevitable that the models and motions of many virtual characters will be cloned. However, the perceptual impact of this trade-off has never been studied. In this paper, we consider the ways in which an impression of variety can be created and the perceptual consequences of certain design choices. In a series of experiments designed to test people's perception of variety in crowds, we found that clones of appearance are far easier to detect than motion clones. Furthermore, we established that cloned models can be masked by color variation, random orientation, and motion. Conversely, the perception of cloned motions remains unaffected by the model on which they are displayed. Other factors that influence the ability to detect clones were examined, such as proximity, model type and characteristic motion. Our results provide novel insights and useful thresholds that will assist in creating more realistic, heterogeneous crowds.

Skinning of skeletally deformable models is extensively used for real-time animation of characters, creatures and similar objects. The standard solution, linear blend skinning, has some serious drawbacks that require artist intervention. Therefore, a number of alternatives have been proposed in recent years. All of them successfully combat some of the artifacts, but none challenge the simplicity and efficiency of linear blend skinning. As a result, linear blend skinning is still the number one choice for the majority of developers. In this paper, we present a novel GPU-friendly skinning algorithm based on dual quaternions. We show that this approach solves the artifacts of linear blend skinning at minimal additional cost. Upgrading an existing animation system (e.g., in a videogame) from linear to dual quaternion skinning is very easy and has negligible impact on run-time performance.

Matrix palette skinning (also known as skeletal subspace deformation) is a very popular real-time animation technique. So far, it has only been applied to the class of quasi-articulated objects, such as moving human or animal figures. In this paper, we demonstrate how to automatically construct skinning approximations of arbitrary precomputed animations, such as those of cloth or elastic materials. In contrast to previous approaches, our method is particularly well suited to input animations without rigid components. Our transformation fitting algorithm finds optimal skinning transformations (in a least-squares sense) and therefore achieves considerably higher accuracy for non-quasi-articulated objects than previous methods. This allows the advantages of skinned animations (e.g., efficient rendering, rest-pose editing and fast collision detection) to be exploited for arbitrary deformations.

The simulation of large crowds of humans is important in many fields of computer graphics, including real-time applications such as games, as they can breathe life into otherwise static scenes and enhance believability. We present a novel hybrid rendering system for crowds that solves the classic problem of degraded quality of image-based representations at close distances by building an impostor rendering system on top of a full, geometry-based, human animation system. This enables almost imperceptible switching between the two representations based on a "pixel to texel" ratio, with minimal popping artefacts. Seamless interchanges are further facilitated by exploiting programmable graphics hardware to efficiently enhance the realism and variety of the dynamically-lit impostors, thereby also improving on existing impostor techniques. To test our system, our virtual crowds are embedded in an urban simulation system (as shown in Figure 1). The results demonstrate a system capable of rendering large realistic crowds with the visual realism of a high-resolution geometry rendering system, but at a fraction of the rendering cost.

In this paper, we consider the problem of determining feature saliency for three-dimensional (3D) objects and describe a series of experiments that examined if salient features exist and can be predicted in advance. We attempt to determine salient features by using an eye-tracking device to capture human gaze data and then investigate if the visual fidelity of simplified polygonal models can be improved by emphasizing the detail of salient features identified in this way. To try to evaluate the visual fidelity of the simplified models, a set of naming time, matching time, and forced-choice preference experiments were carried out. We found that perceptually weighted simplification led to a significant increase in visual fidelity for the lower levels of detail (LOD) of the natural objects, but that for the man-made artifacts the opposite was true. We, therefore, conclude that visually prominent features may be predicted in this way for natural objects, but our results show that saliency prediction for synthetic objects is more difficult, perhaps because it is more strongly affected by task. As a further step we carried out some confirmation experiments to examine if the prominent features found during the saliency experiment were actually the features focused upon during the naming, matching, and forced-choice preference tasks. Results demonstrated that the heads of natural objects received a significant amount of attention, especially during the naming task. We hope that our results will lead to new insights into the nature of saliency in 3D graphics.

Attention is an important factor in the perception of static and dynamic scenes, which should, therefore, be taken into account when creating graphical images and animation. Recently, researchers have recognized this fact and have been investigating how the focus of attention can be measured, predicted, and exploited in graphical systems. In this article, we explore some preliminary strategies for developing an automatic means of predicting and exploiting attention in the processing of collisions and other dynamic events. Recent work on the perception of causality has shown that attention can change the way in which a dynamic scene consisting of collision events is perceived. We describe a series of experiments designed to determine the source of biases in the perception of anomalous collision dynamics and, in particular, whether attention plays a role. Using an eye-tracker, eye-movements were recorded while participants viewed animations of simple causal launching events in 3D involving two colliding spheres. Results indicated that there was indeed a definite pattern to the allocation of attention based on the nature of the event, which is promising for the goal of developing a predictive metric. As a follow-up, a paper-based experiment was carried out in which participants were asked to sketch the predicted post-collision trajectories of the same two spheres printed on paper. These experiments demonstrated that attention alone was not sufficient in determining performance, but rather the nature of the dynamic event itself also played a role.

The simulation of large crowds of humans is important in many fields of computer graphics, including real-time applications such as games, as they can breathe life into otherwise static scenes and enhance believability. Although many new games are released each year, it is very unusual to find large-scale crowds populating the environments depicted. Such applications need to deal with having limited resources available at each frame. With many hundreds or thousands of potential virtual humans in a crowd, traditional techniques rapidly become overwhelmed and are not able to sustain an interactive frame-rate. Therefore, simpler approaches to the rendering, animation and behaviour control of the crowds are needed. Additionally, these new approaches must provide for variety, as environments inhabited by carbon-copy clones can be disconcerting and unrealistic.

Abstract We present a novel method for representing the electrical depolarisation and repolarisation of cardiac cells using real‐time volumetric animation techniques. The visual representation coincides with automatically synchronized electrocardiogram (ECG) input. Whilst other projects in similar fields use super‐computers or vastly complex cellular structures, one of the aims of this project is to simplify the process for real‐time optimization. The current techniques are being used in a teaching tool and also are being implemented in a myocardial infarction diagnostic tool. Cellular automata are used to demonstrate the electrical progression with a multi‐pass nearest‐neighbour algorithm and automatic ECG recognition and detection algorithms are used to synchronize the animation with the inputted ECG. We use texture‐based slices to represent the myocardial volume to improve speed. Copyright © 2004 John Wiley & Sons, Ltd.

Hierarchical object representations play an important role in performing efficient collision handling. Many different geometric primitives have been used to construct these representations, which allow areas of interaction to be localized quickly. For time-critical algorithms, there are distinct advantages to using hierarchies of spheres, known as sphere-trees, for object representation. This article presents a novel algorithm for the construction of sphere-trees. The algorithm presented approximates objects, both convex and non-convex, with a higher degree of fit than existing algorithms. In the lower levels of the representations, there is almost an order of magnitude decrease in the number of spheres required to represent the objects to a given accuracy.

We present a system for the automatic generation of bottom-up visual attention behaviours in virtual humans. Bottom-up attention refers to the way in which the environment solicits one's attention without regard to task-level goals. Our framework is based on the interactions of multiple components: a synthetic vision system for perceiving the virtual world, a model of bottom-up attention for early visual processing of perceived stimuli, a memory system for the storage of previously sensed data and a gaze controller for the generation of resultant behaviours. Our aim is to provide a feeling of presence in inhabited virtual environments by endowing agents with the ability to pay attention to their surroundings.

No abstract available.

This sketch gives an overview of the completed and ongoing research involved in developing a 3D multi-user environment on low-end mobile devices.

For many systems that produce physically based animations, plausibility rather than accuracy is acceptable. We consider the problem of evaluating the visual quality of animations in which physical parameters have been distorted or degraded, either unavoidably due to real-time frame-rate requirements, or intentionally for aesthetic reasons. To date, no generic means of evaluating or predicting the fidelity, either physical or visual, of the dynamic events occurring in an animation exists. As a first step towards providing such a metric, we present a set of psychophysical experiments that established some thresholds for human sensitivity to dynamic anomalies, including angular, momentum and spatio-temporal distortions applied to simple animations depicting the elastic collision of two rigid objects. In addition to finding significant acceptance thresholds for these distortions under varying conditions, we identified some interesting biases that indicate non-symmetric responses to these distortions (e.g., expansion of the angle between post-collision trajectories was preferred to contraction and increases in velocity were preferred to decreases). Based on these results, we derived a set of probability functions that can be used to evaluate the visual fidelity of a physically based simulation. To illustrate how our results could be used, two simple case studies of simulation levels of detail and constrained dynamics are presented.

Abstract Work on levels of detail for human simulation has occurred mainly on a geometrical level, either by reducing the numbers of polygons representing a virtual human, or replacing them with a two‐dimensional imposter. Approaches that reduce the complexity of motions generated have also been proposed. In this paper, we describe ongoing development of a framework for Adaptive Level Of Detail for Human Animation (ALOHA), which incorporates levels of detail for not only geometry and motion, but also includes a complexity gradient for natural behaviour, both conversational and social. ACM CSS: I.3.7 Three‐Dimensional Graphics and Realism—Animation

Abstract A memory model based on ``stage theory'', an influential concept of memory from the field of cognitive psychology,is presented for application to autonomous virtual humans. The virtual human senses external stimuli througha synthetic vision system. The vision system incorporates multiple modes of vision in order to accommodate aperceptual attention approach. The memory model is used to store perceived and attended object information atdifferent stages in a filtering process. The methods outlined in this paper have applications in any area wheresimulation‐based agents are used: training, entertainment, ergonomics and military simulations to name but afew. ACM CSS: I. 3.7 Computer Graphics‐‐Virtual reality

Level of Detail (LOD) techniques for real-time rendering and related perceptual issues have received a lot of attention in recent years. Researchers have also begun to look at the issue of perceptually adaptive techniques for plausible physical simulations. In this article, we are particularly interested in the problem of realistic collision simulation in scenes where large numbers of objects are colliding and processing must occur in real-time. An interruptible and therefore degradable collision-handling mechanism is used and the perceptual impact of this degradation is explored. We look for ways in which we can optimize the realism of such simulations and describe a series of psychophysical experiments that investigate different factors affecting collision perception, including eccentricity, separation, distractors, causality, and accuracy of physical response. Finally, strategies for incorporating these factors into a perceptually adaptive real-time simulation of large numbers of visually similar objects are presented.

Cross-sectional profiles of medieval mouldings are often considered to be a vital form of data for art historians. Mouldings have often been used to highlight salient areas of buildings, and they provide a wealth of information. To date, little use of computer technology has been made in their acquisition, storage and analysis. The paper presents a representation for such planar curves, and an algorithm for its extraction from planar curves. The representation uses geometric primitives, specifically line and ore segments, which allow the curves to be represented in a compact form, approximating the circular sections more effectively than using linear segments alone. These idealised models provide templates which provide a means for the identification of specific features, within profiles, which can then serve as characteristics for use in case-based reasoning.

Interactive simulation is made possible in many applications by simplifying or culling the finer details that would make real‐time performance impossible. This paper examines detail simplification in the specific problem of collision handling for rigid body animation. We present an automated method for calculating consistent collision response at different levels of detail. The mechanism works closely with a system which uses a pre‐computed hierarchical volume model for collision detection.