Crowd simulation is an essential tool in the modern animation toolkit with a wide variety of applications, ranging from urban planning and evacuation analysis to creating believable crowds in both film and games. Modelling the movements and behaviours exhibited in crowds of characters often relies heavily upon the fundamental representation of agents used in the simulation. We propose a mesh-adaptive deformable representation of agents to increase simulation fidelity, generate novel behaviour, and support diversity. Most existing methods use static primitive geometries to represent agents, which neglects the variety in character meshes and animation states. We present an efficient method for generating elliptical particles, which can deform to any mesh and animation state in real-time. The method is straightforward, robust, and exceptionally generalizable. We develop a novel steering methodology for our agent representation method that solves the subsequent challenges of incorporating dynamic asymmetric particle representations. The physically-based algorithm features predictive collision avoidance, incorporating an activation function that encodes the rotational uncertainty of agents. Our model exhibits realistic packing behaviour of agents under high-density conditions, as well as unpacking when the flow is unconstrained. In addition to the compelling qualitative behaviour generated by our model, we present statistical comparisons with existing methods. Our method intrinsically supports the steering of crowds of diverse mesh morphologies without ad-hoc character-specific rules, while affording artist-defined steering components in the character mesh.

Abstract Chiroptera behavior is complex and often unseen as bats are nocturnal, small, and elusive animals. Chiroptology has led to significant insights into the behavior and environmental interactions of bats. Biology, ecology, and even digital media often benefit from mathematical models of animals including humans. However, the history of Chiroptera modeling is often limited to specific behaviors, species, or biological functions and relies heavily on classical modeling methodologies that may not fully represent individuals or colonies well. This work proposes a continuous, parametric, multiagent, Chiroptera behavior model that captures the latest research in echolocation, hunting, and energetics of bats. This includes echolocation‐based perception (or lack thereof), hunting patterns, roosting behavior, and energy consumption rates. We proposed the integration of these mathematical models in a framework that affords the individual simulation of bats within large‐scale colonies. Practitioners can adjust the model to account for different perceptual affordances or patterns among species of bats, or even individuals (such as sickness or injury). We show that our model closely matches results from the literature, affords an animated graphical simulation, and has utility in simulation‐based studies.

Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.

The visualization of spaces, both virtual and built, has long been an important part of the environment design process. Industry tools to visualize occupancy have grown from simple drop-in stock photos post-design to real-time crowds simulations. However, while treatment of visualization and collaborative design processes has long been discussed in the HCI and Architecture communities, these inclusive design methods are infrequently seen in architecture education (e.g. studio) and practice, nor implemented in licensure requirements – leaving designers to think about the future occupants on their own. While there are strong indicators of the impact visualization modality and rendering style have on perception of scale and space, little has been explored regarding how we represent the human form with respect to these design tools and practices. We present findings from a novel online interactive space planning and estimation study that examines the effects of 3 common building visualization modalities in the design process with 3 human form modalities extracted from the architecture literature. Results indicate the type of visualization changes the number of occupants estimated, and that designers prefer integrated manikins within building models when estimating space usage, although their acceptance was equally divided between 2D and 3D. Our findings lay the foundation for new and focused design tools integrating human form and factors at building scale.

Abstract Signage systems play an essential role in ensuring safe, stress‐free, and efficient navigation for the occupants of indoor spaces. Crowd simulations with sufficiently realistic virtual humans provide a convenient and cost‐effective approach to evaluating and optimizing signage systems. In this work, we develop an agent model which makes use of image processing on parametric saliency maps to visually identify signage and distractions in the agent's field of view. Information from identified signs is incorporated into a grid‐based representation of wayfinding familiarity, which is used to guide informed exploration of the agent's environment using a modified A* algorithm. In areas with low wayfinding familiarity, the agent follows a random exploration behaviour based on sampling a grid of previously observed locations for heuristic values based on space syntax isovist measures. The resulting agent design is evaluated in a variety of test environments and found to be able to reliably navigate towards a goal location using a combination of signage and random exploration.

Modeling visual attention is an important aspect of simulating realistic virtual humans. This work proposes a parametric model and method for generating real-time saliency maps from the perspective of virtual agents which approximate those of vision-based saliency approaches. The model aggregates a saliency score from user-defined parameters for objects and characters in an agent’s view and uses that to output a 2D saliency map which can be modulated by an attention field to incorporate 3D information as well as a character’s state of attentiveness. The aggregate and parameterized structure of the method allows the user to model a range of diverse agents. The user may also expand the model with additional layers and parameters. The proposed method can be combined with normative and pathological models of the human visual field and gaze controllers, such as the recently proposed model of egocentric distractions for casual pedestrians that we use in our results.

Abstract At present, environment designers mostly use their intuition and experience to predictively account for how environments might support dynamic activity. The majority of Computer‐Aided Design tools only provide a static representation of space which potentially ignores the impact that an environment layout produces on its occupants and their movements. To address this, computational techniques such as crowd simulation have been developed. With few exceptions, crowd simulation frameworks are often decoupled from environment modeling tools. They usually require specific hardware/software infrastructures and expertise to be used, hindering the designers' abilities to seamlessly simulate, analyze, and incorporate movement‐centric dynamics into their design workflows. To bridge this disconnect, we devise a cross‐browser service‐based simulation analytics platform to analyze environment layouts with respect to occupancy and activity. Our platform allows users to access simulation services by uploading three‐dimensional environment models in numerous common formats, devise targeted simulation scenarios, run simulations, and instantly generate crowd‐based analytics for their designs. We conducted a case study to showcase cross‐domain applicability of our service‐based platform, and a user study to evaluate the usability of this approach.

In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training.

Many multi-agent navigation approaches make use of simplified representations such as a disk. These simplifications allow for fast simulation of thousands of agents but limit the simulation accuracy and fidelity. In this paper, we propose a fully integrated physical character control and multi-agent navigation method. In place of sample complex online planning methods, we extend the use of recent deep reinforcement learning techniques. This extension improves on multi-agent navigation models and simulated humanoids by combining Multi-Agent and Hierarchical Reinforcement Learning. We train a single short term goal-conditioned low-level policy to provide directed walking behaviour. This task-agnostic controller can be shared by higher-level policies that perform longer-term planning. The proposed approach produces reciprocal collision avoidance, robust navigation, and emergent crowd behaviours. Furthermore, it offers several key affordances not previously possible in multi-agent navigation including tunable character morphology and physically accurate interactions with agents and the environment. Our results show that the proposed method outperforms prior methods across environments and tasks, as well as, performing well in terms of zero-shot generalization over different numbers of agents and computation time.

The use of mobile devices is one of the most commonly observed family of distracted behaviours exhibited by pedestrians in urban environments. We develop an event-driven behaviour tree model for distracted pedestrians that includes initiating mobile device use as well as terminating or pausing mobile device use based on internal or external cues to refocus attention. We present a simple, probabilistic attention model for such pedestrians. The proposed model is not meant to be complete. It primarily focuses on computing the probability that a distracted agent looks up, based on the agent’s individual characteristics and the elements in their environment. We condition the potentially attention grabbing elements in the environment on distraction-specific egocentric fields for visual attention. We also propose an oriented ellipse model for capturing the affects of cognitively fuzzy goals during distracted navigation. Our model is simple and intuitively parameterized, and thus can be easily edited and extended.

Crowd simulations provide a practical approach to evaluate building design alternatives with respect to human-centric criteria, such as evacuation times and flow in case of emergency scenarios. Coupled with Building Information Modeling (BIM) tools, they support architects’ iterative exploration of design alternatives. However, methods based on manually configuring a design and a corresponding simulation are not practical for exploring the potentially very large number of design solutions that satisfy human-centric design goals and requirements. Often, for practical reasons, designers may consider standard crowd configurations which do not capture the behavior of diverse occupants that may exhibit different locomotion abilities, movement patterns, and social behaviors. We posit that a joint exploration of high-dimensional building design and occupancy features is necessary to more accurately capture the mutual relations between buildings and the behavior of their occupants. To test this hypothesis, we conducted a series of experiments to automatically explore joint high dimensional design–occupancy patterns using an unsupervised pattern recognition technique (i.e. K-MEANS). We demonstrate that joint design–occupancy explorations provide more accurate results compared with sequential exploration processes that consider default design or crowd features, despite the longer computational times to simulate a large number of solutions. The findings of this case study have practical applications to the design of next-generation design exploration tools that support human-centric analyses in architectural design.

Abstract Authoring behavior narratives for heterogeneous multiagent virtual humans engaged in collaborative, localized, and task‐based behaviors can be challenging. Traditional behavior authoring frameworks are either space‐centric, where occupancy parameters are specified; behavior‐centric, where multiagent behaviors are defined; or agent‐centric, where desires and intentions drive agents' behavior. In this paper, we propose to integrate these approaches into a unique framework to author behavior narratives that progressively satisfy time‐varying building‐level occupancy specifications, room‐level behavior distributions, and agent‐level motivations using a prioritized resource allocation system. This approach can generate progressively more complex and plausible narratives that satisfy spatial, behavioral, and social constraints. Possible applications of this system involve computer gaming and decision‐making in engineering and architectural design.

We present a computational spatial analytics tool for designing environments that better support human-related factors. Our system performs both static and dynamic analyses: the first relates to the building geometry and organization, while the second additionally considers the crowd movement in the space. The results are presented to the designers in the form of numerical values, traces and heat maps displayed on top of the floor plan. We demonstrate our approach with a user study whereby novice architects have tested the proposed approach to iteratively improve a building accessibility in real-time with respect to a selected number of static and dynamic metrics. The results indicate that the users were able to successfully improve their design solutions and thus generate more human-aware environments. The usability and effectiveness of the tool where also measured, yielding positive scores. The modular and flexible nature of the tool enables further extension to incorporate additional static and dynamic spatial metrics.

Floor plan designs and their spatial analysis are typically constrained to blueprints and 2D projections of 3D models. Computing appropriate spatial measures from such representations provides a standard way of quantifying important aspects of the design. We wish to investigate whether a person's perceptual exploration of a space would agree with such spatial measures, that is, whether a person can roughly infer such measures by exploring a space. We perform two studies, one involving novices and the other experts. First, we conduct a perceptual study to discover whether a novice user's perception of spatial measures depends on the mode used to explore the space. Our analysis considers three spatial measures, grounded in Space-Syntax, that characterize key aspects of a design such as visibility, accessibility, and organization. We compare three modes of exploration: 2D blueprints, first-person view in a 3D simulation, and a 3D virtual reality simulation with teleportation. A correlation analysis between the users' perceptual ratings and the spatial measures, indicates that virtual reality is the most effective of the three methods, while 2D blueprints and 3D first-person exploration often fail entirely to convey the spatial measures. In the second study, experts are asked to evaluate and rank the design blueprints for each measure. The expert observations are in strong agreement with the spatial measures for accessibility and organization, but not for visibility in some cases. This indicates that even experts have difficulty understanding spatial aspects of an architecture design from 2D blueprints alone.

Crowd-aware environment design is a complex combinatorial decision process, where small changes in a design may affect crowd flow patterns in unexpected and potentially unintuitive ways. Existing solutions rely on expert intuition, best practices, or automation. To address the dimensionality and complexity of the design process, we propose leveraging automation and human creativity at a large scale akin to crowd sourcing, within a gamified collaborative design framework. Using our system, "players" (novice users or experts) can rapidly iterate on their designs while soliciting feedback from computer simulations of crowd movement and the designs of other players. Our approach affords a new way of thinking of the solution space in that it inherently supports competitive collaboration, co-design, and crowd sourced solutions. We evaluate our framework through a preliminary user study.

Abstract Traffic and pedestrian dynamics communities often use a standard qualitative classification, namely, level of service (LoS), to describe the relationship between the crowd flow and crowd density in an environment. However, this classification has not yet been rigorously studied in the application of synthetic crowds, which are derived using a variety of approaches and may model certain behaviors better than others. Although synthetic crowds can be simulated to extrapolate crowd flow for rigorous quantitative analysis, these may be at odds with the qualitative LoS. In order to successfully use computer‐assisted design, it is important to have sound quantitative metrics as the basis for analysis and optimization. In this paper, we present a systematic empirical analysis of LoS for synthetic crowds. Using established crowd simulation techniques, we quantify the relation between crowd density and crowd flow for evacuation scenarios across different simulators to explore conformity to qualitative LoS classifications. Following this study, we perform environment optimization experiments under various LoS conditions. Finally, we test the generality of optimizing under these LoS conditions. Our results motivate the need for further study, using real and synthetic crowd datasets across representative environment benchmarks.

Abstract We present crowd‐optimized design of environments (CODE): a “crowd‐aware” computational tool for designing environments (e.g., building floor plans). Our system analyses the impact of newly added environment elements (e.g., pillars or doorways) on the resulting crowd flow, using current‐generation crowd simulators. The results of the simulation are used to provide feedback to the designer in terms of aggregate statistics and heat maps. Additionally, our system is able to “automatically” optimize the placement of environment elements to maximize crowd flow in egress scenarios, while satisfying constraints that are imposed by the designer. Using CODE, architects and environment designers can iteratively refine upon their original design to quickly accommodate the dynamic properties of crowd simulations in an interactive fashion. CODE is modular and flexible so that designers may build environments, select from different crowd simulators, and specify varying crowd configurations.

Level of service (LoS) is a standard indicator, widely used in crowd management and urban design, for characterizing the service afforded by environments to crowds of specific densities. However, current LoS indicators are qualitative and rely on expert analysis. Computational approaches for crowd analysis and environment design require robust measures for characterizing the relationship between environments and crowd flow.

Abstract The layout of a building, real or virtual, affects the flow patterns of its intended users. It is well established, for example, that the placement of pillars at proper locations can often facilitate pedestrian flow during the evacuation of a building. Such considerations are therefore important for architects, game level developers, and others whose domains involve agents navigating through buildings. In this paper, we take the first steps towards developing a simulation framework that can be used to study the optimal placement of architectural elements, such as pillars or doors, for the purposes of facilitating dense pedestrian flow during the evacuation of a building. In particular, we show that the steering algorithms used to model the local navigation abilities of the agents significantly affect the results, which motivates the need for a statistically valid approach and further study. Copyright © 2015 John Wiley & Sons, Ltd.

Balancing the interactions between game level design and intended player experience is a difficult and time consuming process. Automating aspects of this process with respect to user-defined constraints has beneficial implications for game designers. A change in level layout may affect the available routes and subsequent player interactions for a number of agents within the level. Small changes in the placement of game elements may lead to significant changes in terms of the challenge experienced by the player on the path to their goal. Estimating the effect of this change requires that the designer take into account new paths of all interacting agents and how these may affect the player. As the number of these agents grow to crowd size, estimating the effect of these changes becomes grows difficult. We present a user-in-the-loop framework for tackling this task by optimizing enemy agent settings and the placement of game elements that affect the flow of agents within the level, with respect to estimated difficulty. Using static path analysis we estimate difficulty based on agent interactions with the player. To exemplify the usefulness of the framework, we show that small changes in level layout lead to significant changes in game difficulty, and optimizations with respect to the characterization of difficulty can be used to attain desired difficulty levels.