Abstract Crowd authoring has mainly focused on generalised agent interactions such as collision avoidance and grouping. However, in society, people interact more intentionally with specific “sources” such as exhibits, or inspectors. Uncovering these interactions is essential for understanding and characterising social behaviours. We propose a model that learns from trajectories, the localised agent interactions imposed by the context of the object or agent source. Our model decomposes agent paths into sequential combinations of simple and understandable “core” behaviours, like approach, stop, and circle around, temporally dissecting source-centric trajectories into standardised movements. We train on pairs of trajectory-encoded images and their associated core behaviour combination. Given a set of trajectories around a specific source, our framework can be applied to build a behaviour distribution, summarising how people interact with the source type. The inferred distribution can then be sampled to generate diverse crowds of context-aware agents. We evaluate our model using collected ground-truth data and perform a case study that showcases the utility of this decomposition of context-aware interactions in other tasks, such as measuring behaviour similarity.

Abstract Our goal is to generate realistic human motion from natural language. Modern methods often face a trade‐off between model expressiveness and text‐to‐motion (T2M) alignment. Some align text and motion latent spaces but sacrifice expressiveness; others rely on diffusion models producing impressive motions but lacking semantic meaning in their latent space. This may compromise realism, diversity and applicability. Here, we address this by combining latent diffusion with a realignment mechanism, producing a novel, semantically structured space that encodes the semantics of language. Leveraging this capability, we introduce the task of textual motion inversion to capture novel motion concepts from a few examples. For motion synthesis, we evaluate LEAD on HumanML3D and KIT‐ML and show comparable performance to the state‐of‐the‐art in terms of realism, diversity and text‐motion consistency. Our qualitative analysis and user study reveal that our synthesised motions are sharper, more human‐like and comply better with the text compared to modern methods. For motion textual inversion (MTI), our method demonstrates improvements in capturing out‐of‐distribution characteristics in comparison to traditional VAEs.

Abstract Simulating believable crowds for applications like movies or games is challenging due to the many components that comprise a realistic outcome. Users typically need to manually tune a large number of simulation parameters until they reach the desired results. We introduce MPACT, a framework that leverages image‐based encoding to convert unlabelled crowd data into meaningful and controllable parameters for crowd generation. In essence, we train a parameter prediction network on a diverse set of synthetic data, which includes pairs of images and corresponding crowd profiles. The learned parameter space enables: (a) implicit crowd authoring and control, allowing users to define desired crowd scenarios using real‐world trajectory data, and (b) crowd analysis, facilitating the identification of crowd behaviours in the input and the classification of unseen scenarios through operations within the latent space. We quantitatively and qualitatively evaluate our framework, comparing it against real‐world data and selected baselines, while also conducting user studies with expert and novice users. Our experiments show that the generated crowds score high in terms of simulation believability, plausibility and crowd behaviour faithfulness.

Efficient disaster victim detection (DVD) in urban areas after natural disasters is crucial for minimizing losses. However, conventional search and rescue (SAR) methods often experience delays, which can hinder the timely detection of victims. SAR teams face various challenges, including limited access to debris and collapsed structures, safety risks due to unstable conditions, and disrupted communication networks. In this paper, we present DeepSafe, a novel two-level deep learning approach for multilevel classification and object detection using a simulated disaster victim dataset. DeepSafe first employs YOLOv8 to classify images into victim and non-victim categories. Subsequently, Detectron2 is used to precisely locate and outline the victims. Experimental results demonstrate the promising performance of DeepSafe in both victim classification and detection. The model effectively identified and located victims under the challenging conditions presented in the dataset. DeepSafe offers a practical tool for real-time disaster management and SAR operations, significantly improving conventional methods by reducing delays and enhancing victim detection accuracy in disaster-stricken urban areas.

Abstract Driven by recent advancements in Extended Reality (XR), the hype around the Metaverse, and real‐time computer graphics, the transformation of the performing arts, particularly in digitizing and visualizing musical experiences, is an ever‐evolving landscape. This transformation offers significant potential in promoting inclusivity, fostering creativity, and enabling live performances in diverse settings. However, despite its immense potential, the field of Virtual Instrument Performances (VIP) has remained relatively unexplored due to numerous challenges. These challenges arise from the complex and multi‐modal nature of musical instrument performances, the need for high precision motion capture under occlusions including the intricate interactions between a musician's body and fingers with instruments, the precise synchronization and seamless integration of various sensory modalities, accommodating variations in musicians' playing styles, facial expressions, and addressing instrument‐specific nuances. This comprehensive survey delves into the intersection of technology, innovation, and artistic expression in the domain of virtual instrument performances. It explores musical performance multi‐modal databases and investigates a wide range of data acquisition methods, encompassing diverse motion capture techniques, facial expression recording, and various approaches for capturing audio and MIDI data (Musical Instrument Digital Interface). The survey also explores Music Information Retrieval (MIR) tasks, with a particular emphasis on the Musical Performance Analysis (MPA) field, and offers an overview of various works in the realm of Musical Instrument Performance Synthesis (MIPS), encompassing recent advancements in generative models. The ultimate aim of this survey is to unveil the technological limitations, initiate a dialogue about the current challenges, and propose promising avenues for future research at the intersection of technology and the arts.

Simulating crowds with realistic behaviors is a difficult but very important task for a variety of applications. Quantifying how a person balances between different conflicting criteria such as goal seeking, collision avoidance and moving within a group is not intuitive, especially if we consider that behaviors differ largely between people. Inspired by recent advances in Deep Reinforcement Learning, we propose Guided REinforcement Learning (GREIL) Crowds, a method that learns a model for pedestrian behaviors which is guided by reference crowd data. The model successfully captures behaviors such as goal seeking, being part of consistent groups without the need to define explicit relationships and wandering around seemingly without a specific purpose. Two fundamental concepts are important in achieving these results: (a) the per agent state representation and (b) the reward function. The agent state is a temporal representation of the situation around each agent. The reward function is based on the idea that people try to move in situations/states in which they feel comfortable in. Therefore, in order for agents to stay in a comfortable state space, we first obtain a distribution of states extracted from real crowd data; then we evaluate states based on how much of an outlier they are compared to such a distribution. We demonstrate that our system can capture and simulate many complex and subtle crowd interactions in varied scenarios. Additionally, the proposed method generalizes to unseen situations, generates consistent behaviors and does not suffer from the limitations of other data-driven and reinforcement learning approaches.

Synthesizing human motion with a global structure, such as a choreography, is a challenging task. Existing methods tend to concentrate on local smooth pose transitions and neglect the global context or the theme of the motion. In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre. In addition, our framework enables generation of diverse motions that are controlled by the content of the music, and not only by the beat. Our music-driven dance synthesis framework is a hierarchical system that consists of three levels: pose, motif, and choreography. The pose level consists of an LSTM component that generates temporally coherent sequences of poses. The motif level guides sets of consecutive poses to form a movement that belongs to a specific distribution using a novel motion perceptual-loss. And the choreography level selects the order of the performed movements and drives the system to follow the global structure of a dance genre. Our results demonstrate the effectiveness of our music-driven framework to generate natural and consistent movements on various dance types, having control over the content of the synthesized motions, and respecting the overall structure of the dance.

Abstract Data‐driven skeletal animation relies on the existence of a suitable learning scheme, which can capture the rich context of motion. However, commonly used motion representations often fail to accurately encode the full articulation of motion, or present artifacts. In this work, we address the fundamental problem of finding a robust pose representation for motion, suitable for deep skeletal animation, one that can better constrain poses and faithfully capture nuances correlated with skeletal characteristics. Our representation is based on dual quaternions, the mathematical abstractions with well‐defined operations, which simultaneously encode rotational and positional orientation, enabling a rich encoding, centered around the root. We demonstrate that our representation overcomes common motion artifacts, and assess its performance compared to other popular representations. We conduct an ablation study to evaluate the impact of various losses that can be incorporated during learning. Leveraging the fact that our representation implicitly encodes skeletal motion attributes, we train a network on a dataset comprising of skeletons with different proportions, without the need to retarget them first to a universal skeleton, which causes subtle motion elements to be missed. Qualitative results demonstrate the usefulness of the parameterization in skeleton‐specific synthesis.

Abstract Recent advancements in crowd simulation unravel a wide range of functionalities for virtual agents, delivering highly‐realistic, natural virtual crowds. Such systems are of particular importance to a variety of applications in fields such as: entertainment (e.g., movies, computer games); architectural and urban planning; and simulations for sports and training. However, providing their capabilities to untrained users necessitates the development of authoring frameworks. Authoring virtual crowds is a complex and multi‐level task, varying from assuming control and assisting users to realise their creative intents, to delivering intuitive and easy to use interfaces, facilitating such control. In this paper, we present a categorisation of the authorable crowd simulation components, ranging from high‐level behaviours and path‐planning to local movements, as well as animation and visualisation. We provide a review of the most relevant methods in each area, emphasising the amount and nature of influence that the users have over the final result. Moreover, we discuss the currently available authoring tools (e.g., graphical user interfaces, drag‐and‐drop), identifying the trends of early and recent work. Finally, we suggest promising directions for future research that mainly stem from the rise of learning‐based methods, and the need for a unified authoring framework.

Designing computer games requires a player-centered approach. Whilst following guidelines and functional requirement specifications is part of the process, observing and measuring qualities of the players experience is key in providing feedback to game designers. Moreover, it can also be used to create adaptive and personalized experiences for players. With the advancement of affective computing and gaming user interfaces, the opportunity to recognize the player's emotions becomes more feasible and each different modality can offer additional information as affect expression is less defined as compared to action selection. This paper explores the use of 3D skeleton motion data transformed to 2D images that encode pose and movement dynamics to represent annotated emotions. The 2D images are then used to train and test the Inception V3 CNN model on a binary classification emotion recognition between happy and sad emotions. Preliminary results in unseen test data indicate that the above transformation technique can capture emotional information. The paper also discusses future directions that may improve the effectiveness of the proposed method on a wider scale.

‘‘Humans at rest tend to stay at rest. Humans in motion tend to cross the road – Isaac Newton.” Even though this response is meant to be a joke to indicate the answer is quite obvious, this important feature of real world crowds is rarely considered in simulations. Answering this question involves several things such as how agents balance between reaching goals, avoid collisions with heterogeneous entities and how the environment is being modeled. As part of a preliminary study, we introduce a reinforcement learning framework to train pedestrians to cross streets with bidirectional traffic. Our initial results indicate that by using a very simple goal centric representation of agent state and a simple reward function, we can simulate interesting behaviors such as pedestrians crossing the road through crossings or waiting for cars to pass.

Abstract Real‐time three‐dimensional (3D) pose estimation is of high interest in interactive applications, virtual reality, activity recognition, and most importantly, in the growing gaming industry. In this work, we present a method that captures and reconstructs the 3D skeletal pose and motion articulation of multiple characters using a monocular RGB camera. Our method deals with this challenging, but useful, task by taking advantage of the recent development in deep learning that allows two‐dimensional (2D) pose estimation of multiple characters and the increasing availability of motion capture data. We fit 2D estimated poses, extracted from a single camera via OpenPose, with a 2D multiview joint projections database that is associated with their 3D motion representations. We then retrieve the 3D body pose of the tracked character, ensuring throughout that the reconstructed movements are natural, satisfy the model constraints, are within a feasible set, and are temporally smooth without jitters. We demonstrate the performance of our method in several examples, including human locomotion, simultaneously capturing of multiple characters, and motion reconstruction from different camera views.

When designing environments in Immersive Virtual Reality, virtual humans are often used to enrich them. In this paper, we research factors arising by the use of virtual crowds that may instigate more user participation in virtual reality scenarios. In particular, we examine whether implementing responsive virtual crowd behaviors toward the participant provides cues that increase the feeling of presence. The second factor we investigate is whether using appearance characteristics to a virtual crowd enable users to not only to identify as being socially related with the virtual characters, but also behave as such in a virtual environment, a factor we refer to as Group membership. We present an experiment in a Virtual Environment (VE) populated with virtual crowd with a violent incident where the user could intervene, aimed to determine how these factors contribute to the enhancement of plausibility and feeling of presence. Our results show that in IVR, virtual crowds with Responsive behavior can increase the feeling of Presence since the user tends to make more interventions when the virtual crowd is responsive towards and when the user is socially related to the incident's victim.

Abstract Inverse kinematics (IK) is the use of kinematic equations to determine the joint parameters of a manipulator so that the end effector moves to a desired position; IK can be applied in many areas, including robotics, engineering, computer graphics and video games. In this survey, we present a comprehensive review of the IK problem and the solutions developed over the years from the computer graphics point of view. The paper starts with the definition of forward and IK, their mathematical formulations and explains how to distinguish the unsolvable cases, indicating when a solution is available. The IK literature in this report is divided into four main categories: the analytical , the numerical , the data‐driven and the hybrid methods. A timeline illustrating key methods is presented, explaining how the IK approaches have progressed over the years. The most popular IK methods are discussed with regard to their performance, computational cost and the smoothness of their resulting postures, while we suggest which IK family of solvers is best suited for particular problems. Finally, we indicate the limitations of the current IK methodologies and propose future research directions.

Many analysis tasks for human motion rely on high-level similarity between sequences of motions, that are not an exact matches in joint angles, timing, or ordering of actions. Even the same movements performed by the same person can vary in duration and speed. Similar motions are characterized by similar sets of actions that appear frequently. In this paper we introduce motion motifs and motion signatures that are a succinct but descriptive representation of motion sequences. We first break the motion sequences to short-term movements called motion words, and then cluster the words in a high-dimensional feature space to find motifs. Hence, motifs are words that are both common and descriptive, and their distribution represents the motion sequence. To cluster words and find motifs, the challenge is to define an effective feature space, where the distances among motion words are semantically meaningful, and where variations in speed and duration are handled. To this end, we use a deep neural network to embed the motion words into feature space using a triplet loss function. To define a signature, we choose a finite set of motion-motifs, creating a bag-of-motifs representation for the sequence. Motion signatures are agnostic to movement order, speed or duration variations, and can distinguish fine-grained differences between motions of the same class. We illustrate examples of characterizing motion sequences by motifs, and for the use of motion signatures in a number of applications.

Abstract This study examines attributes of virtual human behavior that may increase the plausibility of a simulated crowd and affect the user's experience in Virtual Reality. Purpose‐developed experiments in both Immersive and semi‐Immersive Virtual Reality systems queried the impact of collision and basic interaction between real‐users and the virtual crowd and their effect on the apparent realism and ease of navigation within Virtual Reality (VR). Participants' behavior and subjective measurements indicated that facilitating collision avoidance between the user and the virtual crowd makes the virtual characters, the environment, and the whole Virtual Reality system appear more realistic and lifelike. Adding basic social interaction, such as verbal salutations, gaze, and other gestures by the virtual characters towards the user, further contributes to this effect, with the participants reporting a stronger sense of presence. On the other hand, enabling collision avoidance on its own produces a reduced feeling of comfort and ease of navigation in VR. Objective measurements showed another interesting finding that collision avoidance may reduce the user's performance regarding their primary goal (navigating in VR following someone) and that this performance is further reduced when both collision avoidance and social interaction are facilitated.

Abstract Forward and Backward Reaching Inverse Kinematics (FABRIK) is a recent iterative inverse kinematics solver that became very popular because of its simplicity, convergence speed and control performance, especially in models with multiple end effectors. In this paper, we extend and/or adjust FABRIK to be used in problems with leaf joints and closed‐loop chains and to control a fixed inter‐joint distance in a kinetic chain with unsteady data. In addition, we provide optimisation solutions when the target is unreachable and a proof of convergence when a solution is available. We also present various techniques for constraining anthropometric and robotic joint models using FABRIK and provide clarifications and solutions to many questions raised since the first publication of FABRIK. Finally, a human‐like model that has been structured hierarchically and sequentially using FABRIK is presented, utilising most of the suggested joint models; it can efficiently trace targets in real time, without oscillations or discontinuities, verifying the effectiveness of FABRIK. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper we compare participants' behavior and experience when navigating through a virtual environment populated with virtual agents in an IVR (Immersive Virtual Reality) system and a semi-IVR system. We measured the impact of collision and basic interaction between participants and virtual agents in both systems. Our findings show that it is more important for our semi-IVR systems to facilitate collision avoidance between the user and the virtual agents accompanied with basic interaction between them. This can increase the sense of presence and make the virtual agents and the environment appear more realistic and lifelike.

Abstract The increasing availability of large motion databases, in addition to advancements in motion synthesis, has made motion indexing and classification essential for better motion composition. However, in order to achieve good connectivity in motion graphs, it is important to understand human behaviour; human movement though is complex and difficult to completely describe. In this paper, we investigate the similarities between various emotional states with regards to the arousal and valence of the Russell's circumplex model. We use a variety of features that encode, in addition to the raw geometry, stylistic characteristics of motion based on Laban Movement Analysis (LMA). Motion capture data from acted dance performances were used for training and classification purposes. The experimental results show that the proposed features can partially extract the LMA components, providing a representative space for indexing and classification of dance movements with regards to the emotion. This work contributes to the understanding of human behaviour and actions, providing insights on how people express emotional states using their body, while the proposed features can be used as complement to the standard motion similarity, synthesis and classification methods.

Abstract We present a data‐driven method for the real‐time synthesis of believable steering behaviours for virtual crowds. The proposed method interlinks the input examples into a structure we call the perception‐action graph (PAG) which can be used at run‐time to efficiently synthesize believable virtual crowds. A virtual character's state is encoded using a temporal representation, the Temporal Perception Pattern (TPP). The graph nodes store groups of similar TPPs whereas edges connecting the nodes store actions (trajectories) that were partially responsible for the transformation between the TPPs. The proposed method is being tested on various scenarios using different input data and compared against a nearest neighbours approach which is commonly employed in other data‐driven crowd simulation systems. The results show up to an order of magnitude speed‐up with similar or better simulation quality.

Abstract We present a novel approach for analyzing the quality of multi‐agent crowd simulation algorithms. Our approach is data‐driven, taking as input a set of user‐defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real‐time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely‐used methods, including a simulation from a commercial game.

When preparing high dynamic range images (HDR) for display on standard monitors, it is often necessary to make a choice between global and local tone mapping. While the former is simple and efficient, it may fail to reproduce details in high contrast image regions. Although, the latter can better reproduce details in such regions, it often comes at the cost of increased complexity and computational time. In this paper, we present an algorithm that combines the best of both approaches. We perform local tone mapping only in high frequency image regions where the visibility of details can be an issue. In low frequency regions, we employ global tone mapping to save computational resources without degrading quality. Our algorithm is most suitable for tone mapping operators (TMOs) that utilize the concept of local adaptation luminances.

Exergames do not have the capacity to detect whether the players are really enjoying the game-play. The games are not intelligent enough to detect significant emotional states and adapt accordingly in order to offer a better user experience for the players. We propose a set of body motion features, based on the Effort component of Laban Movement Analysis (LMA), that are used to provide sets of classifiers for emotion recognition in a game scenario for four emotional states:concentration, meditation, excitement and frustration. Experimental results show that, the system is capable of successfully recognizing the four different emotional states at a very high rate.

Given a noisy and incomplete point set, we introduce a method that simultaneously recovers a set of locally fitted primitives along with their global mutual relations. We operate under the assumption that the data corresponds to a man-made engineering object consisting of basic primitives, possibly repeated and globally aligned under common relations. We introduce an algorithm to directly couple the local and global aspects of the problem. The local fit of the model is determined by how well the inferred model agrees to the observed data, while the global relations are iteratively learned and enforced through a constrained optimization. Starting with a set of initial RANSAC based locally fitted primitives, relations across the primitives such as orientation, placement, and equality are progressively learned and conformed to. In each stage, a set of feasible relations are extracted among the candidate relations, and then aligned to, while best fitting to the input data. The global coupling corrects the primitives obtained in the local RANSAC stage, and brings them to precise global alignment. We test the robustness of our algorithm on a range of synthesized and scanned data, with varying amounts of noise, outliers, and non-uniform sampling, and validate the results against ground truth, where available.

Abstract Many times, even if a crowd simulation looks good in general, there could be some specific individual behaviors which do not seem correct. Spotting such problems manually can become tedious, but ignoring them may harm the simulation's credibility. In this paper we present a data‐driven approach for evaluating the behaviors of individuals within a simulated crowd. Based on video‐footage of a real crowd, a database of behavior examples is generated. Given a simulation of a crowd, an analog analysis is performed on it, defining a set of queries, which are matched by a similarity function to the database examples. The results offer a possible objective answer to the question of how similar are the simulated individual behaviors to real observed behaviors. Moreover, by changing the video input one can change the context of evaluation. We show several examples of evaluating simulated crowds produced using different techniques and comprising of dense crowds, sparse crowds and flocks.

Abstract Computation of illumination with soft‐shadows from all‐frequency environment maps, is a computationally expensive process. Use of pre‐computation add the limitation that receiver's geometry must be known in advance, since Irradiance computation takes into account the receiver's normal direction. We propose a method that using a new notion that we introduce, the Fullsphere Irradiance, allows us to accumulate the contribution from all light sources in the scene, on a possible receiver without knowing the receiver's geometry. This expensive computation is done in a pre‐processing step. The pre‐computed value is used at run time to compute the Irradiance arriving at any receiver with known direction. We show how using this technique we compute soft‐shadows and self‐shadows in real‐time from all‐frequency environments, with only modest memory requirements. A GPU implementation of the method, yields high frame rates even for complex scenes with dozens of dynamic occluders and receivers.

Abstract We present an example‐based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real‐world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.

The three papers in this special section were presented at the 2005 ACM Virtual Reality Software and Technology (VRST) conference.

Abstract In this paper we revisit the cells‐and‐portals visibility methods, originally developed for the special case of architectural interiors. We define an effectiveness measure for a cells‐and‐portals partition, and introduce a two‐pass algorithm that computes a cells‐and‐portals partition. The algorithm uses a simple heuristic that strives to create small portals as a means for generating an effective partition. The input to the algorithm is a set of half edges in 2D that can be extracted from a complex polygonal model. The first pass of the algorithm creates an initial partition, which is then refined by the second pass. We show that our method creates a partition that is more effective than the common BSP partition, even when the latter is further refined with the application of our second pass. Our cells‐and‐portals algorithm is designed to deal with arbitrarily oriented walls. The algorithm also supports outdoor scenes, where the vertical walls of the buildings serve as occluders and portals are extended above the buildings. We show that the extended portals allow an output‐sensitive rendering of large urban scenes. While most visibility algorithms use graphics hardware in order to cull hidden regions of the model, we examine the usefulness of a hardware‐assisted portal test as opposed to the conventional software test. Finally, since our two‐pass method is fully automatic and local, it supports incremental changes of the model by locally recomputing and updating the partition. We call our method ‘Breaking the Walls’ since it breaks out of indoor scenes to outdoor scenes, and allows walls to be broken interactively, with an instant updating of the partition. Copyright © 2006 John Wiley & Sons, Ltd.

We introduce a new mesh representation for arbitrary surfaces that integrates different levels of detail into the final representation. It is produced after remeshing an existing model and omits storing connectivity information. Switching between resolutions can be instantly accomplished without extra computation. This representation is generated by chartifying initial the mesh, parametrizing and re-meshing each chart using a regular grid of control points in a multilevel approach. Finally, the model becomes watertight by hierarchically stitching each chart's boundary points and normals.

Most of the common approaches for the pedestrian simulation, used in the Graphics/VR community, are bottom-up. The avatars are individually simulated in the space and the overall behavior emerges from their interactions. This can lead to interesting results but it does not scale and can not be applied to populating a whole city. In this paper we present a novel method that can scale to a scene of almost any size. We use a top-down approach where the movement of the pedestrians is computed at a higher level, taking a global view of the model, allowing the flux and densities to be maintained at very little cost at the city level. This information is used for stochastically guiding a more detailed and realistic low level simulation when the user zooms in to a specific region, thus maintaining the consistency. At the heart of the system is an iterative method that models the flow of avatars as a random walk. People are moved around a graph of nodes until the model reaches a steady state which provides feedback for the avatar low level navigation at run time. The Negative Binomial distribution function is used to model the number of people leaving each node while the selected direction is based on the popularity of the nodes through their preference-factor. The preference-factor is a function of a number of parameters including the visibility of a node, the events taking place in it and so on. An important feature of the low-level dynamics is that a user can interactively specify a number of intuitive variables that can predictably modify the collective behavior of the avatars in a region; the density, the flux and the number of people can be selectively modified.

The global scope of the CREATE project is to develop a mixed-reality framework that enables highly interactive real-time construction and manipulation of photo-realistic, virtual worlds based on real data sources. This framework will be tested and applied to cultural heritage content in an educational context, as well as to the design and review of architectural/urban planning settings. The evaluation of the project is based on a human-centered, constructivist approach to working and learning, with special attention paid to the evaluation of the resulting mixed reality experience. Through this approach, participants in an activity "construct" their own knowledge by testing ideas and concepts based on their prior knowledge and experience, applying these to a new situation, and integrating the new knowledge gained with pre-existing intellectual constructs. CREATE project uses a high degree of interactivity, and includes provision for other senses (haptics and sound). The application developed in CREATE are designed to run on different platforms, and the targeted running systems are SGI and PC driven, with immersive stereo-displays such as a workbench, a ReaCTor (CAVE-like environment), and a wide projection screen.

In this paper, we revisit the cells-and-portals visibility methods, originally developed for the special case of architectural interiors. We define an effectiveness measure for a cells-and-portals partitioning, and introduce a two-pass algorithm that computes a cells-and-portals partition. The algorithm uses a simple heuristic that creates short portals as a mean for generating an effective partition. The input to the algorithm is a set of half edges in 2D that can be extracted from a complex polygonal model. The first pass of the algorithm creates an initial partition, which is then refined by the second pass. We show that our method creates a partition that is more effective than the common BSP partition, even when the latter is further refined with the application of our second pass. Our cells-and-portals algorithm is designed to deal with arbitrarily oriented walls. The algorithm also supports outdoor scenes, where the vertical walls of the buildings served as occluders and portals are extended above the buildings. We show that the extended portals allow an output-sensitive rendering of large urban scenes. Finally, since our two-pass method is fully automatic and local, it supports incremental changes of the model by locally recomputing and updating the partition. We call our method "Breaking the Walls" (BW) since it breaks out of indoor scenes to outdoor scenes, and allows walls to be broken interactively, with an instant updating of the partition.

Visibility algorithms for walkthrough and related applications have grown into a significant area, spurred by the growth in the complexity of models and the need for highly interactive ways of navigating them. In this survey, we review the fundamental issues in visibility and conduct an overview of the visibility culling techniques developed in the last decade. The taxonomy we use distinguishes point-based methods from-region methods. Point-based methods are further subdivided into object and image-precision techniques, while from-region approaches can take advantage of the cell-and-portal structure of architectural environments or handle generic scenes.

Abstract Real‐time crowd visualization has recently attracted quite an interest from the graphics community and, asinteractive applications become even more complex, there is a natural demand for new and unexplored applicationscenarios. However, the interactive simulation of complex environments populated by large numbers of virtualcharacters is a composite problem which poses serious difficulties even on modern computer hardware. In thispaper we look at methods to deal with various aspects of crowd visualization, ranging from collision detectionand behaviour modeling to fast rendering with shadows and quality shading. These methods make extensive useof current graphics hardware capabilities with the aim of providing scalability without compromising run‐timespeed. Results from a system employing these techniques seem to suggest that simulations of reasonably complexenvironments populated with thousands of animated characters are possible in real‐time. ACM CSS: I.3.7 Three‐Dimensional Graphics and Realism—Animation

In this paper, we address the problem of shadow computation for large environments including thousands of dynamic objects. The method we propose is based on the assumption that the environment is 2.5D, which is often the case for virtual cities, thus avoiding complex visibility computation. We apply our method for virtual cities populated by thousands of walking humans, which we render with impostors, allowing real time simulation.In this paper, we treat the cases of shadows cast by buildings on humans, and by humans on the ground. To avoid 3D computation, we represent the shadows cast by buildings onto the environment with a 2.5D shadow map. When humans move, we quickly access the shadow information at the current location with a 2D grid. For each new position of a human, we compute its coverage by the shadow, and we render the shadow on top of the impostor with low cost using multi-texturing hardware. We also use the property of an impostor to display the shadow of humans on the ground plane, by projecting the impostor relatively to the light source.The method is currently limited to sharp shadows and a single light source. However approximations could be made to allow non-accurate soft-shadows. We show in the results that the computation of the shadows, as well as the display is done in real time, and that the method could be easily extended to real time moving light sources.

This paper describes a fast technique for animating clothing on walking humans. It exploits a mass-spring cloth model but applies a new velocity directional modification approach to overcome its super-elasticity. The algorithm for cloth-body collision detection and response is based on image-space interference tests, unlike the existing ones that use object-space checks. The modern workstations' graphics hardware is used not only to compute the depth maps of the body but also to interpolate the body normal vectors and velocities of each vertex. As a result the approach is very fast and makes it possible to produce animation at a rate of three to four frames per second.

In virtual reality simulations the speed of rendering is vitally important. One of the techniques for controlling the frame rate is the assignment of different levels of detail for each object within a scene. The most well-known level of detail assignment algorithms are the Funkhouser[1] algorithm and the algorithm where the level of detail is assigned with respect to the distance of the object from the viewer.

Ray tracing and Monte-Carlo based global illumination, as well as radiosity and other finite-element based global illumination methods, all require repeated evaluation of quantitative visibility queries, such as: what is the average visibility between a point (a differential area element) and a finite area or volume; or what is the average visibility between two finite areas or volumes. We present a new data structure and an algorithm for rapidly evaluating such queries in complex scenes. The proposed approach utilizes a novel image-based discretization of the space of bounded rays in the scene, constructed in a preprocessing stage. This data structure makes it possible to quickly compute approximate answers to visibility queries. Because visibility queries are computed using a discretization of the space, the execution time is effectively decoupled from the number of geometric primitives in the scene. A potential hazard with the proposed approach is that it might require large amounts of memory, if the data structures are designed in a naive fashion. We discuss ways for representing the discretization in a compact manner while still allowing rapid query evaluation. Preliminary results demonstrate the effectiveness of the proposed approach.

No abstract available.

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This paper presents an algorithm for shadow calculation in dynamic polyhedral scenes illuminated by point light sources. It is based on a modification of Shadow Volume Binary Space Partition trees, to allow these to be constructed from the original scene polygons in arbitrary order and to support for fast reconstruction after a change in scene geometry. Timings using sample scenes are presented that indicate substantial savings both in terms of computation time and shadows produced.

Abstract This paper investigates a new method for dynamically changing Binary Space Partition (BSP) trees. A BSP tree representation of a 3D polygonal scene provides an ideal data structure for rapidly performing the hidden surface computations involved in changing the viewpoint. However, BSP trees have generally been thought to be unsuitable for applications where the geometry of objects in the scene changes dynamically. The purpose of this paper is to introduce a dynamic BSP tree algorithm which does allow for such changes, and which maintains the simplicity and integrity of the BSP tree representation. The algorithm is extended to include dynamic changes to shadows. We calibrate the algorithms by transforming a range of objects in a scene, and reporting on the observed timing results.