A survey of real-time crowd rendering

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

Output-Sensitive Rendering of Detailed Animated Characters for Crowd Simulation

High-quality, detailed animated characters are often represented as textured polygonal meshes. The problem with this technique is the high cost that involves rendering and animating each one of these characters. This problem has become a major limiting factor in crowd simulation. Since we want to render a huge number of characters in real-time, the purpose of this thesis is therefore to study the current existing approaches in crowd rendering to derive a novel approach. The main limitations we have found when using impostors are (1) the big amount of memory needed to store them, which also has to be sent to the graphics card, (2) the lack of visual quality in close-up views, and (3) some visibility problems. As we wanted to overcome these limitations, and improve performance results, the found conclusions lead us to present a new representation for 3D animated characters using relief mapping, thus supporting an output-sensitive rendering. The basic idea of our approach is to encode each character through a small collection of textured boxes storing color and depth values. At runtime, each box is animated according to the rigid transformation of its associated bone in the animated skeleton. A fragment shader is used to recover the original geometry using an adapted version of relief mapping. Unlike competing output-sensitive approaches, our compact representation is able to recover high-frequency surface details and reproduces view-motion parallax e ects. Furthermore, the proposed approach ensures correct visibility among di erent animated parts, and it does not require us to prede ne the animation sequences nor to select a subset of discrete views. Finally, a user study demonstrates that our approach allows for a large number of simulated agents with negligible visual artifacts

Using image morphing for memory-efficient impostor rendering on GPU

Real-time rendering of large animated crowds consisting thousands of virtual humans is important for several applications including simulations, games and interactive walkthroughs; but cannot be performed using complex polygonal models at interactive frame rates. For that reason, several methods using large numbers of pre-computed image-based representations, which are called as impostors, have been proposed. These methods take the advantage of existing programmable graphics hardware to compensate the computational expense while maintaining the visual fidelity. Making the number of different virtual humans, which can be rendered in real-time, not restricted anymore by the required computational power but by the texture memory consumed for the variety and discretization of their animations. In this work, we proposed an alternative method that reduces the memory consumption by generating compelling intermediate textures using image-morphing techniques. In order to demonstrate the preserved perceptual quality of animations, where half of the key-frames were rendered using the proposed methodology, we have implemented the system using the graphical processing unit and obtained promising results at interactive frame rates

Output-Sensitive Rendering of Detailed Animated Characters for Crowd Simulation

High-quality, detailed animated characters are often represented as textured polygonal meshes. The problem with this technique is the high cost that involves rendering and animating each one of these characters. This problem has become a major limiting factor in crowd simulation. Since we want to render a huge number of characters in real-time, the purpose of this thesis is therefore to study the current existing approaches in crowd rendering to derive a novel approach. The main limitations we have found when using impostors are (1) the big amount of memory needed to store them, which also has to be sent to the graphics card, (2) the lack of visual quality in close-up views, and (3) some visibility problems. As we wanted to overcome these limitations, and improve performance results, the found conclusions lead us to present a new representation for 3D animated characters using relief mapping, thus supporting an output-sensitive rendering. The basic idea of our approach is to encode each character through a small collection of textured boxes storing color and depth values. At runtime, each box is animated according to the rigid transformation of its associated bone in the animated skeleton. A fragment shader is used to recover the original geometry using an adapted version of relief mapping. Unlike competing output-sensitive approaches, our compact representation is able to recover high-frequency surface details and reproduces view-motion parallax e ects. Furthermore, the proposed approach ensures correct visibility among di erent animated parts, and it does not require us to prede ne the animation sequences nor to select a subset of discrete views. Finally, a user study demonstrates that our approach allows for a large number of simulated agents with negligible visual artifacts

Scalable Real-Time Rendering for Extremely Complex 3D Environments Using Multiple GPUs

In 3D visualization, real-time rendering of high-quality meshes in complex 3D environments is still one of the major challenges in computer graphics. New data acquisition techniques like 3D modeling and scanning have drastically increased the requirement for more complex models and the demand for higher display resolutions in recent years. Most of the existing acceleration techniques using a single GPU for rendering suffer from the limited GPU memory budget, the time-consuming sequential executions, and the finite display resolution. Recently, people have started building commodity workstations with multiple GPUs and multiple displays. As a result, more GPU memory is available across a distributed cluster of GPUs, more computational power is provided throughout the combination of multiple GPUs, and a higher display resolution can be achieved by connecting each GPU to a display monitor (resulting in a tiled large display configuration). However, using a multi-GPU workstation may not always give the desired rendering performance due to the imbalanced rendering workloads among GPUs and overheads caused by inter-GPU communication. In this dissertation, I contribute a multi-GPU multi-display parallel rendering approach for complex 3D environments. The approach has the capability to support a high-performance and high-quality rendering of static and dynamic 3D environments. A novel parallel load balancing algorithm is developed based on a screen partitioning strategy to dynamically balance the number of vertices and triangles rendered by each GPU. The overhead of inter-GPU communication is minimized by transferring only a small amount of image pixels rather than chunks of 3D primitives with a novel frame exchanging algorithm. The state-of-the-art parallel mesh simplification and GPU out-of-core techniques are integrated into the multi-GPU multi-display system to accelerate the rendering process

Generating, animating, and rendering varied individuals for real-time crowds

To simulate realistic crowds of virtual humans in real time, three main requirements need satisfaction. First of all, quantity, i.e., the ability to simulate thousands of characters. Secondly, quality, because each virtual human composing a crowd needs to look unique in its appearance and animation. Finally, efficiency is paramount, for an operation usually efficient on a single virtual human, becomes extremely costly when applied on large crowds. Developing an architecture able to manage all three aspects is a challenging problem that we have addressed in our research. Our first contribution is an efficient and versatile architecture called YaQ, able to simulate thousands of characters in real time. This platform, developed at EPFL-VRLab, results from several years of research and integrates state-of-the-art techniques at all levels: YaQ aims at providing efficient algorithms and real-time solutions for populating globally and massively large-scale empty environments. YaQ thus fits various application domains, such as video games and virtual reality. Our architecture is especially efficient in managing the large quantity of data that is used to simulate crowds. In order to simulate large crowds, many instances of a small set of human templates have to be generated. From this starting point, if no care is taken to vary each character individually, many clones appear in the crowd. We present several algorithms to make each individual unique in the crowd. Firstly, we introduce a new method to distinguish body parts of a human and apply detailed color variety and patterns to each one of them. Secondly, we present two techniques to modify the shape and profile of a virtual human: a simple and efficient method for attaching accessories to individuals, and efficient tools to scale the skeleton and mesh of an instance. Finally, we also contribute to varying individuals' animation by introducing variations to the upper body movements, thus allowing characters to make a phone call, have a hand in their pocket, or carry heavy accessories, etc. To achieve quantity in a crowd, levels of detail need to be used. We explore the most adequate solutions to simulate large crowds with levels of detail, while avoiding disturbing switches between two different representations of a virtual human. To do so, we develop solutions to make most variety techniques scalable to all levels of detail

Gaze Behaviors for Virtual Crowd Characters

Nowadays, crowds of virtual characters are used in many domains such as neurosciences, psychology, and computer sciences. Since as human beings, we are natural experts in human being representation and movement, it makes it that much harder to correctly model and animate virtual characters. This becomes even more challenging when considering crowds of virtual characters. Indeed, in addition to the representation and animation, there is the mandatory trade-off between rich, realistic behaviors and computational costs. In this paper, we present a crowd engine, to which we introduce and extra layer which allows its characters to produce gaze behaviors. We thus enhance crowd realism by allowing the characters composing it to be aware of their environment and other characters and/or a user

A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units

Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations

Populating Ancient Pompeii with Crowds of Virtual Romans

Pompeii was a Roman city, destroyed and completely buried during an eruption of the volcano Mount Vesuvius. We have revived its past by creating a 3D model of its previous appearance and populated it with crowds of Virtual Romans. In this paper, we detail the process, based on archaeological data, to simulate ancient Pompeii life in real time. In a first step, an annotated city model is generated using procedural modelling. These annotations contain semantic data, such as land usage, building age, and window/door labels. In a second phase, the semantics are automatically interpreted to populate the scene and trigger special behaviors in the crowd, depending on the location of the characters. Finally, we describe the system pipeline, which allows for the simulation of thousands of Virtual Romans in real time

Egocentric affordance fields in pedestrian steering

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