Research Article  Open Access
RealTime Large Crowd Rendering with Efficient Character and Instance Management on GPU
Abstract
Achieving the efficient rendering of a large animated crowd with realistic visual appearance is a challenging task when players interact with a complex game scene. We present a realtime crowd rendering system that efficiently manages multiple types of character data on the GPU and integrates seamlessly with levelofdetail and visibility culling techniques. The character data, including vertices, triangles, vertex normals, texture coordinates, skeletons, and skinning weights, are stored as either buffer objects or textures in accordance with their access requirements at the rendering stage. Our system preserves the viewdependent visual appearance of individual character instances in the crowd and is executed with a finegrained parallelization scheme. We compare our approach with the existing crowd rendering techniques. The experimental results show that our approach achieves better rendering performance and visual quality. Our approach is able to render a large crowd composed of tens of thousands of animated instances in real time by managing each type of character data in a single buffer object.
1. Introduction
Crowd rendering is an important form of visual effects. In video games, thousands of computerarticulated polygonal characters with a variety of appearances can be generated to inhabit in a virtual scene like a village, a city, or a forest. Movements of the crowd are usually programmed through a crowd simulator [1–4] with given goals. To achieve a realistic visual approximation of the crowd, each character is usually tessellated with tessellation algorithms [5], which increases the character’s mesh complexity to a sufficient level, so that fine geometric details and smooth mesh deformations can be preserved in the virtual scene. As a result, the virtual scene may end up with a composition of millions of, or even hundreds of millions of, vertices and triangles. Rasterizing such massive amount of vertices and triangles into pixels is a high computational cost. Also, when storing them in memory, the required amount of memory may be beyond the storage capability of a graphic hardware. Thus, in the production of video games [6–9], advanced crowd rendering technologies are needed in order to increase the rendering speed and reduce memory consumption while preserving the crowd’s visual fidelity.
To improve the diversity of character appearances in a crowd, a common method is duplicating a character’s mesh many times and then assigning each duplication with a different texture and a varied animation. Some advanced methods allow developers to modify the shape proportion of duplications and then retarget rigs and animations to the modified meshes [10, 11] or synthesize new motions [12, 13]. With the support of hardwareaccelerated geometryinstancing and pseudoinstancing techniques [9, 14–16], multiple data of a character, including vertices, triangles, textures, skeletons, skinning weights, and animations, can be cached in the memory of a graphics processing unit (GPU). At each time when the virtual scene needs to be rendered, the renderer will alter and assemble those data dynamically without the need of fetching them from CPU main memory. However, storing the duplications on the GPU consumes a large amount of memory and limits the number of instances that can be rendered. Furthermore, even though the instancing technique reduces the CPUGPU communication overhead, it may suffer the lack of dynamic mesh adaption (e.g., continuous levelofdetail).
In this work, we present a rendering system, which achieves a realtime rendering rate for a crowd composed of tens of thousands of animated characters. The system ensures a fully utilization of GPU memory and computational power through the integration with continuous levelofdetail (LOD) and ViewFrustum Culling techniques. The size of memory allocated for each character is adjusted dynamically in response to the change of levels of detail, as the camera’s viewing parameters change. The scene of the crowd may end up with more than one hundred million triangles. Different from existing instancing techniques, our approach is capable of rendering all different characters through a single buffer object for each type of data. The system encapsulates multiple data of each unique source characters into buffer objects and textures, which can then be accessed quickly by shader programs on the GPU as well as maintained efficiently by a generalpurpose GPU programming framework.
The rest of the paper is organized as follows. Section 2 reviews the previous works about crowd simulation and crowd rendering. Section 3 gives an overview of our system’s rendering pipeline. In Section 4, we describe fundamentals of continues LOD and animation techniques and discuss their parallelization on the GPU. Section 5 describes how to process and store the source character’s multiple data and how to manage instances on the GPU. Section 6 presents our experimental results and compares our approach with the existing crowd rendering techniques. We conclude our work in Section 7.
2. Related Work
Simulation and rendering are two primary computing components in a crowd application. They are often tightly integrated as an entity to enable a special form of in situ visualization, which in general means data is rendered and displayed by a renderer in real time while a simulation is running and generating new data [17–19]. One example is the work presented by Hernandez et al. [20] that simulated a wandering crowd behavior and visualized it using animated 3D virtual characters on GPU clusters. Another example is the work presented by Perez et al. [21] that simulated and visualized crowds in a virtual city. In this section, we first briefly review some previous work contributing to crowd simulation. Then, more related to our work, we focus on acceleration techniques contributing to crowd rendering, including levelofdetail (LOD), visibility culling, and instancing techniques.
A crowd simulator uses macroscopic algorithms (e.g., continuum crowds [22], aggregate dynamics [23], vector fields [24], and navigation fields [25]) or microscopic algorithms (e.g., morphable crowds [26] and socially plausible behaviors [27]) to create crowd motions and interactions. Outcomes of the simulator are usually a successive sequence of time frames, and each frame contains arrays of positions and orientations in the 3D virtual environment. Each pair of position and orientation information defines the global status of a character at a given time frame. McKenzie et al. [28] developed a crowd simulator to generate noncombatant civilian behaviors which is interoperable with a simulation of modern military operations. Zhou et al. [29] classified the existing crowd modeling and simulation technologies based on the size and time scale of simulated crowds and evaluated them based on their flexibility, extensibility, execution efficiency, and scalability. Zhang et al. [30] presented a unified interaction framework on GPU to simulate the behavior of a crowd at interactive frame rates in a finegrained parallel fashion. Malinowski et al. [31] were able to perform large scale simulations that resulted in tens of thousands of simulated agents.
Visualizing a large number of simulated agents using animated characters is a challenging computing task and worth an indepth study. Beacco et al. [9] surveyed previous approaches for realtime crowd rendering. They reviewed and examined existing acceleration techniques and pointed out that LOD techniques have been used widely in order to achieve high rendering performance, where a faraway character can be represented with a coarse version of the character as the alternative for rendering. A wellknown approach is using discrete LOD representations, which are a set of offline simplified versions of a mesh. At the rendering stage, the renderer selects a desired version and renders it without any additional processing cost at runtime. However, Discrete LODs require too much memory for storing all simplified versions of the mesh. Also, as mentioned by Cleju and Saupe [32], discrete LODs could cause “popping" visual artifacts because of the unsmooth shape transition between simplified versions. Dobbyn et al. [33] introduced a hybrid rendering approach that combines imagebased and geometrybased rendering techniques. They evaluated the rendering quality and performance in an urban crowd simulation. In their approach, the characters in the distance were rendered with imagebased LOD representations, and they were switched to geometric representations when they were within a closer distance. Although the visual quality seemed better than using discrete LOD representations, popping artifacts also occurred when the renderer switches content between image representations and geometric representations. Ulicny et al. [34] presented an authoring tool to create crowd scenes of thousands of characters. To provide users immediate visual feedback, they used lowpoly meshes for source characters. The meshes were kept in OpenGL’s display lists on GPU for fast rendering.
Characters in a crowd are polygonal meshes. The mesh is rigged by a skeleton. Rotations of the skeleton’s joints transform surrounding vertices, and subsequently the mesh can be deformed to create animations. While LOD techniques for simplifying general polygonal meshes have been studied maturely (e.g., progressive meshes [35], quadric error metrics [36]), not many existing works studied how to simplify animated characters. Landreneau and Schaefer [37] presented mesh simplification criteria to preserve deforming features of animations on simplified versions of the mesh. Their approach was developed based on quadric error metrics, and they added simplification criteria with the consideration of vertices’ skinning weights from the joints of the skeleton and the shape deviation between the character’s rest pose and a deformed shape in animations. Their approach produced more accurate animations for dynamically simplified characters than many other LODbased approaches, but it caused a higher computational cost, so it may be challenging to integrate their approach into a realtime application. Willmott [38] presented a rapid algorithm to simplify animated characters. The algorithm was developed based on the idea of vertex clustering. The author mentioned the possibility of implementing the algorithm on the GPU. However, in comparison to the algorithm with progressive meshes, it did not produce wellsimplified characters to preserve fine features of character appearance. Feng et al. [39] employed triangularchar geometry images to preserve the features of both static and animated characters. Their approach achieved high rendering performance by implement geometry images with multiresolutions on the GPU. In their experiment, they demonstrated a realtime rendering rate for a crowd composed of 15.3 million triangles. However, there could be a potential LOD adaptation issue if the geometry images become excessively large. Peng et al. [8] proposed a GPUbased LODenabled system to render crowds along with a novel texturepreserving algorithm on simplified versions of the character. They employed a continuous LOD technique to refine or reduce mesh details progressively during the runtime. However, their approach was based on the simulation of single virtual humans. Instantiating and rendering multiple types of characters were not possible in their system. Savoy et al. [40] presented a webbased crowd rendering system that employed the discrete LOD and instancing techniques.
Visibility culling technique is another type of acceleration techniques for crowd rendering. With visibility culling, a renderer is able to reject a character from the rendering pipeline if it is outside the view frustum or blocked by other characters or objects. Visibility culling techniques do not cause any loss of visual fidelity on visible characters. Tecchia et al. [41] performed efficient occlusion culling for a highly populated scene. They subdivided the virtual environmental map into a 2D grid and used it to build a KDtree of the virtual environment. The large and static objects in the virtual environment, such as buildings, were used as occluders. Then, an occlusion tree was built at each frame and merged with the KDtree. Barczak et al. [42] integrated GPUaccelerated ViewFrustum Culling and occlusion culling techniques into a crowd rendering system. The system used a vertex shader to test whether or not the bounding sphere of a character intersects with the view frustum. A hierarchical Z buffer image was built dynamically in a vertex shader in order to perform occlusion culling. Hernandez and Isaac Rudomin [43] combined ViewFrustum Culling and LOD Selection. Desired detail levels were assigned only to the characters inside the view frustum.
Instancing techniques have been commonly used for crowd rendering. Their execution is accelerated by GPUs with graphics API such as DirectX and OpenGL. Zelsnack [44] presented coding details of the pseudoinstancing technique using OpenGL shader language (GLSL). The pseudoinstancing technique requires perinstance calls sent to and executed on the GPU. Carucci [45] introduced the geometryinstancing technique which renders all vertices and triangles of a crowd scene through a geometry shader using one call. Millan and Rudomin [14] used the pseudoinstancing technique for rendering fulldetail characters which were closer to the camera. The faraway characters with low details were rendered using impostors (an imagebased approach). Ashraf and Zhou [46] used a hardwareaccelerated method through programmable shaders to animated crowds. Klein et al. [47] presented an approach to render configurable instances of 3D characters for the web. They improved XML3D to store 3D content in a more efficient way in order to support an instancingbased rendering mechanism. However, their approach lacked support for multiple character assets.
3. System Overview
Our crowd rendering system first preprocesses source characters and then performs runtime tasks on the GPU. Figure 1 illustrates an overview of our system. Our system integrates ViewFrustum Culling and continuous LOD techniques.
At the preprocessing stage, a finetocoarse progressive mesh simplification algorithm is applied to every source character. In accordance with the edgecollapsing criteria [8, 35], the simplification algorithm selects edges and then collapses them by merging adjacent vertices iteratively and then removes the triangles containing the collapsed edges. The edgecollapsing operations are stored as data arrays on the GPU. Vertices and triangles can be recovered by splitting the collapsed edges and are restored with respect to the order of applying coarsetofine splitting operations. Vertex normal vectors are used in our system to determine a proper shading effect for the crowd. A bounding sphere is computed for each source character. It tightly encloses all vertices in all frames of the character’s animation. The bounding sphere will be used during the runtime to test an instance against the view frustum. Note that bounding spheres may be in different sizes because the sizes of source characters may be different. Other data including textures, UVs, skeletons, skinning weights, and animations are packed into textures. They can be accessed quickly by shader programs and the generalpurpose GPU programming framework during the runtime.
The runtime pipeline of our system is executed on the GPU through five parallel processing components. We use an instance ID in shader programs to track the index of each instance, which corresponds to the occurrence of a source character at a global location and orientation in the virtual scene. A unique source character ID is assigned to each source character, which is used by an instance to index back to the source character that is instantiated from. We assume that the desired number of instances is provided by users as a parameter in the system configuration. The global positions and orientations of instances simulated from a crowd simulator are passed into our system as input. They determine where the instances should occur in the virtual scene. The component of ViewFrustum Culling determines the visibility of instances. An instance will be considered to be visible if its bounding sphere is inside or intersects with the view frustum. The component of LOD Selection determines the desired detail level of the instances. It is executed with an instancelevel parallelization. A detail level is represented as the numbers of vertices and triangles selected which are assembled from the vertex and triangle repositories. The component of LOD Mesh Generation produces LOD meshes using the selected vertices and triangles. The size of GPU memory may not be enough to store all the instances at their finest levels. Thus, a configuration parameter called the primitive budget is passed into the runtime pipeline as a global constraint to ensure the generated LOD meshes fit into the GPU memory. The component of Animating Meshes attaches skeletons and animations to the simplified versions (LOD meshes) of the instances. At the end of the pipeline, the rendering component rasterizes the LOD meshes along with appropriate textures and UVs and displays the result on the screen.
4. Fundamentals of LOD Selection and Character Animation
During the step of preprocessing, the mesh of each source character is simplified by collapsing edges. Same as existing work, the collapsing criteria in our approach preserves features at high curvature regions [39] and avoids collapsing the edges on or crossing texture seams [8]. Edges are collapsed onebyone. We utilized the same method presented in [48], which saved collapsing operations into an array structure suitable for the GPU architecture. The index of each array element represents the source vertex and the value of the element represents the target vertex it merges to. By using the array of edge collapsing, the repositories of vertices and triangles are rearranged in an increasing order, so that, at runtime, the desired complexity of a mesh can be generated by selecting a successive sequence of vertices and triangles from the repositories. Then, the skeletonbased animations are applied to deform the simplified meshes. Figure 2 shows the different levels of detail of several source characters that are used in our work. In this section, we brief the techniques of LOD Selection and character animation.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
4.1. LOD Selection
Let us denote as the total number of instances in the virtual scene. A desired level of details for an instance can be represented as the pair of , where is the desired number of vertices, and is the desired number of triangles. Given a value of , the value of can be retrieved from the prerecorded edgecollapsing information [48]. Thus, the goal of LOD Selection is to determine an appropriate value of for each instance, with considerations of the available GPU memory size and the instance’s spatial relationship to the camera. If an instance is outside the view frustum, is set to zero. For the instances inside the view frustum, we used the LOD Selection metric in [48] to compute , as shown in
Equation (1) is the same as the firstpass algorithm presented by Peng and Cao [48]. It originates from the model perception method presented by Funkhouser et al. [49] and is improved by Peng and Cao [48, 50] to accelerate the rendering of large CAD models. We found that (1) is also a suitable metric for the large crowd rendering. In the equation, refers to the total number of vertices that can be retained on the GPU, which is a userspecified value computed based on the available size of GPU memory. The value of can be tuned to balance the rendering performance and visual quality. is the weight computed with the projected area of the bounding sphere of the th instance on the screen () and the distance to the camera (). is the perception parameter introduced by Funkhouser et al. [49]. In our work, the value of is set to 3.
With and , the successive sequences of vertices and triangles are retrieved from the vertex and triangle repositories of the source character. By applying the parallel triangle reformation algorithm [48, 50], the desired shape of the simplified mesh is generated using the selected vertices and triangles.
4.2. Animation
In order to create character animations, each LOD mesh has to be bound to a skeleton along with skinning weights added to influence the movement of the mesh’s vertices. As a result, the mesh will be deformed by rotating joints of the skeleton. As we mentioned earlier, each vertex may be influenced by a maximum of four joints. We want to note that the vertices forming the LOD mesh are a subset of the original vertices of the source character. There is not any new vertex introduced during the preprocess of mesh simplification. Because of this, we were able to use original skinning weights to influence the LOD mesh. When transformations defined in an animation frame are loaded on the joints, the final vertex position will be computed by summing the weighted transformations of the skinning joints. Let us denote each of the four joints influencing a vertex as , where . The weight of on the vertex is denoted as . Thus, the final position of the vertex , denoted as , can be computed by using
In (2), is the vertex position at the time when the mesh is bound to the skeleton. When the mesh is first loaded without the use of animation data, the mesh is placed in the initial binding pose. When using an animation, the inverse of the binding pose needs to be multiplied by an animated pose. This is reflected in the equation, where is the inverse of binding transformation of the joint , and represents the transformation of the joint from the current frame of the animation. is the transformation representing the instance’s global position and orientation. Note that the transformations , , and are represented in the form of matrix. The weight is a single float value, and the four weight values must sum to 1.
5. Source Character and Instance Management
Geometryinstancing and pseudoinstancing techniques are the primary solutions for rendering a large number of instances, while allowing the instances to have different global transformations. The pseudoinstancing technique is used in OpenGL and calls instances’ drawing functions onebyone. The geometryinstancing technique is included in Direct3D since the version 9 and in OpenGL since version 3.3. It advances the pseudoinstancing technique in terms of reducing the number of drawing calls. It supports the use of a single drawing call for instances of a mesh and therefore reduces the communication cost of sending call requests from CPU to GPU and subsequently increases the performance. As regards data storage on the GPU, buffer objects are used for shader programs to access and update data quickly. A buffer object is a continuous memory block on the GPU and allows the renderer to rasterize data in a retained mode. In particular, a vertex buffer object (VBO) stores vertices. An index buffer object (IBO) stores indices of vertices that form triangles or other polygonal types used in our system. In particular, the geometryinstancing technique requires a single copy of vertex data maintained in the VBO, a single copy of triangle data maintained in the IBO, and a single copy of distinct world transformations of all instances. However, if the source character has a high geometric complexity and there are lots of instances, the geometryinstancing technique may make the uniform data type in shaders hit the size limit, due to the large amount of vertices and triangles sent to the GPU. In such case, the drawing call has to be broken into multiple calls.
There are two types of implementations for instancing techniques: static batching and dynamic batching [45]. The singlecall method in the geometryinstancing technique is implemented with static batching, while the multicall method in both the pseudoinstancing and geometryinstancing techniques are implemented with dynamic batching. In static batching, all vertices and triangles of the instances are saved into a VBO and IBO. In dynamic batching, the vertices and triangles are maintained in different buffer objects and drawn separately. The implementation with static batching has the potential to fully utilize the GPU memory, while dynamic batching would underutilize the memory. The major limitation of static batching is the lack of LOD and skinning supports. This limitation makes the static batching not suitable for rendering animated instances, though it has been proved to be faster than dynamic batching in terms of the performance of rasterizing meshes.
In our work, the storage of instances is managed similarly to the implementation of static batching, while individual instances can still be accessed similarly to the implementation of dynamic batching. Therefore, our approach can be seamlessly integrated with LOD and skinning techniques, while taking the use of a single VBO and IBO for fast rendering. This section describes the details of our contributions for character and instance management, including texture packing, UVguided mesh rebuilding, and instance indexing.
5.1. Packing Skeleton, Animation, and Skinning Weights into Textures
Smooth deformation of a 3D mesh is a computationally expensive process because each vertex of the mesh needs to be repositioned by the joints that influence it. We packed the skeleton, animations, and skinning weights into 2D textures on the GPU, so that shader programs can access them quickly. The skeleton is the binding pose of the character. As explained in (2), the inverse of the binding pose’s transformation is used during the runtime. In our approach, we stored this inverse into the texture as the skeletal information. For each joint of the skeleton, instead of storing individual translation, rotation, and scale values, we stored their composed transformation in the form of a matrix. Each joint’s binding pose transformation matrix takes four RGBA texels for storage. Each RGBA texel stores a row of the matrix. Each channel stores a single element of the matrix. By using OpenGL, matrices are stored as the format of GL_RGBA32F in the texture, which is a 32bit floatingpoint type for each channel in one texel. Let us denote the total number of joints in a skeleton as . Then, the total number of texels to store the entire skeleton is .
We used the same format for storing the skeleton to store an animation. Each animation frame needs texels to store the joints’ transformation matrices. Let us denote the total number of frames in an animation as . Then, the total number of texels for storing the entire animation is . For each animation frame, the matrix elements are saved into successive texels in the row order. Here we want to note that each animation frame starts from a new row in the texture.
The skinning weights of each vertex are four values in the range of , where each value represents the influencing percentage of a skinning joint. For each vertex, the skinning weights require eight data elements, where the first four data elements are joint indices, and the last four are the corresponding weights. In other words, each vertex requires two RGBA texels to store the skinning weights. The first texel is used to store joint indices, and the second texel is used to store weights.
5.2. UVGuided Mesh Rebuilding
A 3D mesh is usually a seamless surface without boundary edges. The mesh has to be cut and unfolded into 2D flatten patches before a texture image can be mapped onto it. To do this, some edges have to be selected properly as boundary edges, from which the mesh can be cut. The relationship between the vertices of a 3D mesh and 2D texture coordinates can be described as a texture mapping function . Inner vertices (those not on boundary edges) have a onetoone texture mapping. In other words, each inner vertex is mapped to a single pair of texture coordinates. For the vertices on boundary edges, since boundary edges are the cutting seams, a boundary vertex needs to be mapped to multiple pairs of texture coordinates. Figure 3 shows an example that unfolds a cube mesh and maps it into a flatten patch in 2D texture space. In the figure, stands for a point in the 2D texture space. Each vertex on the boundary edges is mapped to more than one points, which are , , , and .
(a)
(b)
In a hardwareaccelerated renderer, texture coordinates are indexed from a buffer object, and each vertex should associate with a single pair of texture coordinates. Since the texture mapping function produces more than one pairs of texture coordinates for boundary vertices, we conducted a mesh rebuilding process to duplicate boundary vertices and mapped each duplicated one to a unique texture point. By doing this, although the number of vertices is increased due to the cuttings on boundary edges, the number of triangles is the same as the number of triangles in the original mesh. In our approach, we initialized two arrays to store UV information. One array stores texture coordinates, the other array stores the indices of texture points with respect to the order of triangle storage. Algorithm 1 shows the algorithmic process to duplicate boundary vertices by looping through all triangles. In the algorithm, is the array of original vertices storing 3D coordinates . is the array of original triangles storing the sequence of vertex indices. Similar to , is the array of indices of texture points in 2D texture space and represents the same triangular topology as the mesh. Note that the order of triangle storage for the mesh is the same as the order of triangle storage for the 2D texture patches. is the array of vertex normal vectors. is the total number of original triangles, and is the number of texture points in 2D texture space.

After rebuilding the mesh, the number of vertices in will be identical to the number of texture points , and the array of triangles () is replaced by the array of indices of the texture points ().
5.3. Source Character and Instance Indexing
After applying the data packing and mesh rebuilding methods presented in Sections 5.1 and 5.2, the multiple data of a source character are organized into GPUfriendly data structures. The character’s skeleton, skinning weights, and animations are packed into textures and readonly in shader programs on the GPU. The vertices, triangles, texture coordinates, and vertex normal vectors are stored in arrays and retained on the GPU. During the runtime, based on the LOD Selection result (see Section 4.1), a successive subsequence of vertices, triangles, texture coordinates, and vertex normal vectors are selected for each instance and maintained as single buffer objects. As mentioned in Section 4.1, the simplified instances are constructed in a parallel fashion through a generalpurpose GPU programming framework. Then, the framework interoperates with the GPU’s shader programs and allows shaders to perform rendering tasks for the instances. Because continuous LOD and animated instancing techniques are assumed to be used in our approach, instances have to be rendered onebyone, which is the same as the way of rendering animated instances in geometryinstancing and pseudoinstancing techniques. However, our approach needs to construct the data within one execution call, rather than dealing with perinstance data.
Figure 4 illustrates the data structures of storing VBO and IBO on the GPU. Based on the result of LOD Selection, each instance is associated with a and a (see Section 4.1) that represent the amount of vertices and triangles selected based on the current view setting. We employed CUDA Thrust [51] to process the arrays of and using the prefix sum algorithm in a parallel fashion. As a result, for example, each represents the offset of vertex count prior to the th instance, and the number of vertices for the th instance is .
Algorithm 2 describes the vertex transformation process in parallel in the vertex shader. It transforms the instance’s vertices to their destination positions while the instance is being animated. In the algorithm, represents the total number of source characters. The inverses of the binding pose skeletons are a texture array denoted as . The skinning weights are a texture array denoted as . We used a walk animation for each source character, and the texture array of the animations is denoted as . is the global transformation matrix of the instance in the virtual scene. This algorithm is developed based on the data packing formats described in Section 5.1. Each source character is assigned with a unique source character ID, denoted as in the algorithm. The drawing calls are issued per instance, so is passed into the shader as an input parameter. The function of computes the coordinates in the texture space to locate which texel to fetch. The input of includes the current vertex or joint index () that needs to be mapped, the width () and height () of the texture, and the number of texels () associating with the vertex or joint. For example, to retrieve a vertex’s skinning weights, the is set to 2; to retrieve a joint’s transformation matrix, the is set to 4. In the function of , vertices of the instance are transformed in a parallel fashion by the composed matrix () computed from a weighted sum of matrices of the skinning joints. The function takes a texture and the coordinates located in the texture space as input. It returns the values encoded in texels. The function is usually provided in a shader programming framework. Different from the rendering of static models, animated characters change geometric shapes over time due to continuous pose changes in the animation. In the algorithm, stands for the current frame index of the instance’s animation. is updated in the main code loop during the execution of the program.

6. Experiment and Analysis
We implemented our crowd rendering system on a workstation with Intel i75930K 3.50GHz PC with 64GBytes of RAM and an Nvidia GeForce GTX980 Ti 6GB graphics card. The rendering system is built with Nvidia CUDA Toolkit 9.2 and OpenGL. We employed 14 unique source characters. Table 1 shows the data configurations of those source characters. Each source character is articulated with a skeleton and fully skinned and animated with a walk animation. Each one contains an unfolded texture UV set along with a texture image at the resolution of . We stored those source characters on the GPU. Also, all source characters have been preprocessed by the mesh simplification and animation algorithms described in Section 4. We stored the edgecollapsing information and the character bounding spheres in arrays on the GPU. In total, the source characters require 184.50MB memory for storage. The size of mesh data is much smaller than the size of texture images. The mesh data requires 16.50MB memory for storage, which is only 8.94% of the total memory consumed. At initialization of the system, we randomly assigned a source character to each instance.

6.1. Visual Fidelity and Performance Evaluations
As defined in Section 4.1, is a memory budget parameter that determines the geometric complexity and the visual quality of the entire crowd. For each instance in the crowd, the corresponding bounding sphere is tested against the view frustum to determine its visibility. The value of is only distributed across visible instances.
We created a walkthrough camera path for the rendering of the crowd. The camera path emulates a gaming navigation behavior and produces a total of 1,000 frames. The entire crowd contains 30,000 instances spread out in the virtual scene with predefined movements and moving trajectories. Figure 5 shows a rendered frame of the crowd with the value of set to 1, 6, and 10 million, respectively. The main camera moves on the walkthrough path. The reference camera is aimed at the instances far away from the main camera and shows a closeup view of those faraway instances. Our LODbased instancing method ensures the total number of selected vertices and triangles is within the specified memory budget, while preserving the fine details of instances that are closer to the main camera. Although the faraway instances are simplified significantly, because the long distances to the main camera, their appearance in the main camera do not cause a loss of visual fidelity. Figure 5(a) shows visual appearance of the crowd rendered from the viewpoint of the main camera (top images), in which faraway instances are rendered using the simplified versions (bottom images).
If all instances are rendered at the level of full detail, the total number of triangles would be 169.07 million. Through the simulation of the walkthrough camera path, we had an average of instances inside the view frustum. The maximum and minimum numbers of instances inside the view frustum are 29,967 and 5,038, respectively. We specified different values for . Table 2 shows the performance breakdowns with regard to the runtime processing components in our system. In the table, the “# of Rendered Triangles" column includes the minimum, maximum, and averaged number of triangles selected during the runtime. As we can see, the higher the value of is, the more the triangles are selected to generate simplified instances and subsequently the better visual quality is obtained for the crowd. Our approach is memory efficient. Even when is set to a large value, such as 20 million, the crowd needs only 26.23 million triangles in average, which is only 15.51% of the original number of triangles. When the value of is small, the difference between the averaged and the maximum number of triangles is significant. For example, when is equal to 5 million, the difference is at a ratio () of 73.96%. This indicates that the number of triangles in the crowd varies significantly according to the change of instancecamera relationships (including instances’ distance to the camera and their visibility). This is because a small value of limits the level of details that an instance can reach up. Even if an instance is close to the camera, it may not obtain a sufficient cut from to satisfy a desired detail level. As we can see in the table, when the value of becomes larger than 10 million, the ratio is increased to 94%. The ViewFrustum Culling and LOD Selection components are implemented together, and both are executed in parallel at an instance level. Thus, the execution time of this component does not change as the value of increases. The component of LOD Mesh Generation is executed in parallel at a triangle level. Its execution time increases as the value of increases. Animating Meshes and Rendering components are executed with the acceleration of OpenGL’s buffer objects and shader programs. They are timeconsuming, and their execution time increases as more triangles need to be rendered. Figure 6 shows the change of FPS over different values of . As we can see in the figure, the FPS decreases as the value of increases. When is smaller than 4 million, the decreasing slope of FPS is small. This is because the change on the number of triangles over frames of the camera path is small. When is small, many closeup instances end down to the lowest level of details due to the insufficient memory budget from . When increases from 4 to 17 million, the decreasing slop of FPS becomes larger. This is because the number of triangles over frames of the camera path varies considerably with different values of . As increases beyond 17 million, the decreasing slope becomes smaller again, as many instances including faraway ones reach the full level of details.

6.2. Comparison and Discussion
We analyzed two rendering techniques and compared them against our approach in terms of performance and visual quality. The pseudoinstancing technique minimizes the amount of data duplication by sharing vertices and triangles among all instances, but it does not support LOD on a perinstance level [44, 52]. The pointbased technique renders a complex geometry by using a dense of sampled points in order to reduce the computational cost in rendering [53, 54]. The pseudoinstancing technique does not support ViewFrustum Culling. For the comparison reason, in our approach, we ensured all instances to be inside the view frustum of the camera by setting a fixed position of the camera and setting fixed positions for all instances, so that all instances are processed and rendered by our approach. The complexity of each instance rendered by the pointbased technique is selected based on its distance to the camera which is similar to our LOD method. When an instance is near the camera, the original mesh is used for rendering; when the instance is located far away from the camera, a set of points are approximated as sample points to represent the instance. In this comparison, the pseudoinstancing technique always renders original meshes of instances. We chose two different N values (= 5 million and = 10 million) for rendering with our approach. As shown in Figure 7, our approach results in better performance than the pseudoinstancing technique. This is because the number of triangles rendered by using the pseudoinstancing technique is much larger than the number of triangles determined by the LOD Selection component of our approach. The performance of our approach becomes better than the pointbased technique as the number of increases. Figure 8 shows the comparison of visual quality among our approach, pseudoinstancing technique, and pointbased technique. The image generated from the pseudoinstancing technique represents the original quality. Our approach can achieve better visual quality than the pointbased technique. As we can see in the top images of the figure, the instances far away from the camera rendered by the pointbased technique appear to have “holes" due to the disconnection between vertices. In addition, the popping artifact appears when using the pointbased technique. This is because the technique uses a limited number of detail levels from the technique of discrete LOD. Our approach avoids the popping artifact since continuous LOD representations of the instances are applied during the rendering.
7. Conclusion
In this work, we presented a crowd rendering system that takes the advantages of GPU power for both generalpurpose and graphics computations. We rebuilt the meshes of source characters based on the flatten pieces of texture UV sets. We organized the source characters and instances into buffer objects and textures on the GPU. Our approach is integrated seamlessly with continuous LOD and ViewFrustum Culling techniques. Our system maintains the visual appearance by assigning each instance an appropriate level of details. We evaluated our approach with a crowd composed of 30,000 instances and achieved realtime performance. In comparison with existing crowd rendering techniques, our approach better utilizes GPU memory and reaches a higher rendering frame rate.
In the future, we would like to integrate our approach with occlusion culling techniques to further reduce the number of vertices and triangles during the runtime and improve the visual quality. We also plan to integrate our crowd rendering system with a simulation in a real game application. Currently, we only used a single walk animation in the crowd rendering system. In a real game application, more animation types should be added, and a motion graph should be created in order to make animations transit smoothly from one to another. We also would like to explore possibilities to transplant our approach onto a multiGPU platform, where a more complex crowd could be rendered in real time, with the supports of higher memory capability and more computational power provided by multiple GPUs.
Data Availability
The source character assets used in our experiment were purchased from cgtrader.com. The source code including GPU and shader programs were developed in our research lab by the authors of this paper. The source code has been archived in our lab and available for distribution upon requests. The supplementary video (available here) submitted together with the manuscript shows our experimental result of realtime crowd rendering.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This work was supported by the National Science Foundation Grant CNS1464323. We thank Nvidia for donating the GPU device that has been used in this work to run our approach and produce experimental results. The source characters were purchased from cgtrader.com with the buyer’s license that allows us to disseminate the rendered and moving images of those characters through the software prototypes developed in this work.
Supplementary Materials
We submitted a demo video of our crowd rendering system as a supplementary material. The video consists of two parts. The first part is a recorded video clip showing the realtime rendering result as the camera moves along the predefined walkthrough path in the virtual scene. The bottom view corresponds the user’s view, and the top view is a reference view showing the changes of the view frustum of the user’s view (red lines). In the reference view, the yellow dots represent the character instances outside the view frustum of the user’s view. At the topleft corner of the screen, the runtime performance and data information are plotted. The runtime performance is measured by the frames per second (FPS). The N value is set to 10 million, the total number of instances is set to 30,000, and the total number of original triangles of all instances is 169.07 million. The number of rendered triangles changes dynamically according to the user’s view changes, since the continuous LOD algorithm is used in our approach. The second part of the video explains the visual effect of LOD algorithm. It uses a straightline moving path that allows the main camera moves from one corner of the crowd to another corner at a constant speed. The video is played 4 times faster than the original FPS of the video clip. The topright view shows the entire scene of the crowd. The red lines represent the view frustum of the main camera, which is moving along the straightline path. The blue lines represent the view frustum of the reference camera, which is fixed and aimed at the faraway instances from the main camera. As we can see in the view of reference camera (topleft corner), the simplified faraway instances are gaining more details as the main camera moves towards the location of the reference camera. At the same time, more instances are outside the view frustum of the main camera, represented as yellow dots. (Supplementary Materials)
References
 S. Lemercier, A. Jelic, R. Kulpa et al., “Realistic following behaviors for crowd simulation,” Computer Graphics Forum, vol. 31, no. 2, Part 2, pp. 489–498, 2012. View at: Publisher Site  Google Scholar
 A. Golas, R. Narain, S. Curtis, and M. C. Lin, “Hybrid longrange collision avoidancefor crowd simulation,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 7, pp. 1022–1034, 2014. View at: Publisher Site  Google Scholar
 G. Payet, “Developing a massive realtime crowd simulation framework on the gpu,” 2016. View at: Google Scholar
 I. Karamouzas, N. Sohre, R. Narain, and S. J. Guy, “Implicit crowds: Optimization integrator for robust crowd simulation,” ACM Transactions on Graphics, vol. 36, no. 4, pp. 1–13, 2017. View at: Publisher Site  Google Scholar
 J. R. Shewchuk, “Delaunay refinement algorithms for triangular mesh generation,” Computational Geometry. Theory and Applications, vol. 22, no. 13, pp. 21–74, 2002. View at: Publisher Site  Google Scholar  MathSciNet
 J. Pettré, P. D. H. Ciechomski, J. Maim, B. Yersin, J.P. Laumond, and D. Thalmann, “Realtime navigating crowds: scalable simulation and rendering,” Computer Animation and Virtual Worlds, vol. 17, no. 34, pp. 445–455, 2006. View at: Publisher Site  Google Scholar
 J. Maïm, B. Yersin, J. Pettré, and D. Thalmann, “YaQ: An architecture for realtime navigation and rendering of varied crowds,” IEEE Computer Graphics and Applications, vol. 29, no. 4, pp. 44–53, 2009. View at: Publisher Site  Google Scholar
 C. Peng, S. I. Park, Y. Cao, and J. Tian, “A realtime system for crowd rendering: parallel LOD and texturepreserving approach on GPU,” in Proceedings of the International Conference on Motion in Games, vol. 7060 of Lecture Notes in Computer Science, pp. 27–38, Springer, 2011. View at: Publisher Site  Google Scholar
 A. Beacco, N. Pelechano, and C. Andújar, “A survey of realtime crowd rendering,” Computer Graphics Forum, vol. 35, no. 8, pp. 32–50, 2016. View at: Google Scholar
 R. McDonnell, M. Larkin, S. Dobbyn, S. Collins, and C. O'Sullivan, “Clone attack! Perception of crowd variety,” ACM Transactions on Graphics, vol. 27, no. 3, p. 1, 2008. View at: Publisher Site  Google Scholar
 Y. P. Du Sel, N. Chaverou, and M. Rouillé, “Motion retargeting for crowd simulation,” in Proceedings of the 2015 Symposium on Digital Production, DigiPro ’15, pp. 9–14, ACM, New York, NY, USA, August 2015. View at: Publisher Site  Google Scholar
 F. Multon, L. France, M.P. CaniGascuel, and G. Debunne, “Computer animation of human walking: a survey,” Journal of Visualization and Computer Animation, vol. 10, no. 1, pp. 39–54, 1999. View at: Publisher Site  Google Scholar
 S. Guo, R. Southern, J. Chang, D. Greer, and J. J. Zhang, “Adaptive motion synthesis for virtual characters: a survey,” The Visual Computer, vol. 31, no. 5, pp. 497–512, 2015. View at: Publisher Site  Google Scholar
 E. Millán and I. Rudomn, “Impostors, pseudoinstancing and image maps for gpu crowd rendering,” The International Journal of Virtual Reality, vol. 6, no. 1, pp. 35–44, 2007. View at: Google Scholar
 H. Nguyen, “Chapter 2: Animated crowd rendering,” in Gpu Gems 3, AddisonWesley Professional, 2007. View at: Google Scholar
 H. Park and J. Han, “Fast rendering of large crowds using GPU,” in Entertainment Computing  ICEC 2008, S. M. Stevens and S. J. Saldamarco, Eds., vol. 5309 of Lecture Notes in Computer Science, pp. 197–202, Springer, Berlin, Germany, 2009. View at: Publisher Site  Google Scholar
 K.L. Ma, “In situ visualization at extreme scale: challenges and opportunities,” IEEE Computer Graphics and Applications, vol. 29, no. 6, pp. 14–19, 2009. View at: Publisher Site  Google Scholar
 T. Sasabe, S. Tsushima, and S. Hirai, “Insitu visualization of liquid water in an operating PEMFC by soft Xray radiography,” International Journal of Hydrogen Energy, vol. 35, no. 20, pp. 11119–11128, 2010, Hyceltec 2009 Conference. View at: Publisher Site  Google Scholar
 H. Yu, C. Wang, R. W. Grout, J. H. Chen, and K.L. Ma, “In situ visualization for largescale combustion simulations,” IEEE Computer Graphics and Applications, vol. 30, no. 3, pp. 45–57, 2010. View at: Publisher Site  Google Scholar
 B. Hernandez, H. Perez, I. Rudomin, S. Ruiz, O. DeGyves, and L. Toledo, “Simulating and visualizing realtime crowds on GPU clusters,” Computación y Sistemas, vol. 18, no. 4, pp. 651–664, 2015. View at: Publisher Site  Google Scholar
 H. Perez, I. Rudomin, E. A. Ayguade, B. A. Hernandez, J. A. EspinosaOviedo, and G. VargasSolar, “Crowd simulation and visualization,” in Proceedings of the 4th BSC Severo Ochoa Doctoral Symposium, Poster, May 2017. View at: Google Scholar
 A. Treuille, S. Cooper, and Z. Popović, “Continuum crowds,” ACM Transactions on Graphics, vol. 25, no. 3, pp. 1160–1168, 2006. View at: Publisher Site  Google Scholar
 R. Narain, A. Golas, S. Curtis, and M. C. Lin, “Aggregate dynamics for dense crowd simulation,” in ACM SIGGRAPH Asia 2009 Papers, SIGGRAPH Asia ’09, p. 1, Yokohama, Japan, December 2009. View at: Publisher Site  Google Scholar
 X. Jin, J. Xu, C. C. L. Wang, S. Huang, and J. Zhang, “Interactive control of largecrowd navigation in virtual environments using vector fields,” IEEE Computer Graphics and Applications, vol. 28, no. 6, pp. 37–46, 2008. View at: Publisher Site  Google Scholar
 S. Patil, J. Van Den Berg, S. Curtis, M. C. Lin, and D. Manocha, “Directing crowd simulations using navigation fields,” IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 2, pp. 244–254, 2011. View at: Publisher Site  Google Scholar
 E. Ju, M. G. Choi, M. Park, J. Lee, K. H. Lee, and S. Takahashi, “Morphable crowds,” ACM Transactions on Graphics, vol. 29, no. 6, pp. 1–140, 2010. View at: Google Scholar
 S. I. Park, C. Peng, F. Quek, and Y. Cao, “A crowd modeling framework for socially plausible animation behaviors,” in Motion in Games, M. Kallmann and K. Bekris, Eds., vol. 7660 of Lecture Notes in Computer Science, pp. 146–157, Springer, Berlin, Germany, 2012. View at: Publisher Site  Google Scholar
 F. D. McKenzie, M. D. Petty, P. A. Kruszewski et al., “Integrating crowdbehavior modeling into military simulation using game technology,” Simulation Gaming, vol. 39, no. 1, Article ID 1046878107308092, pp. 10–38, 2008. View at: Publisher Site  Google Scholar
 S. Zhou, D. Chen, W. Cai et al., “Crowd modeling and simulation technologies,” ACM Transactions on Modeling and Computer Simulation (TOMACS), vol. 20, no. 4, pp. 1–35, 2010. View at: Publisher Site  Google Scholar
 Y. Zhang, B. Yin, D. Kong, and Y. Kuang, “Interactive crowd simulation on GPU,” Journal of Information and Computational Science, vol. 5, no. 5, pp. 2341–2348, 2008. View at: Google Scholar
 A. Malinowski, P. Czarnul, K. Czuryło, M. Maciejewski, and P. Skowron, “Multiagent largescale parallel crowd simulation,” Procedia Computer Science, vol. 108, pp. 917–926, 2017, International Conference on Computational Science, ICCS 2017, 1214 June 2017, Zurich, Switzerland. View at: Publisher Site  Google Scholar
 I. Cleju and D. Saupe, “Evaluation of suprathreshold perceptual metrics for 3D models,” in Proceedings of the 3rd Symposium on Applied Perception in Graphics and Visualization, APGV ’06, pp. 41–44, ACM, New York, NY, USA, July 2006. View at: Publisher Site  Google Scholar
 S. Dobbyn, J. Hamill, K. O'Conor, and C. O'Sullivan, “Geopostors: A realtime geometry/impostor crowd rendering system,” in Proceedings of the 2005 Symposium on Interactive 3D Graphics and Games, I3D ’05, pp. 95–102, ACM, New York, NY, USA, April 2005. View at: Publisher Site  Google Scholar
 B. Ulicny, P. d. Ciechomski, and D. Thalmann, “Crowdbrush: interactive authoring of realtime crowd scenes,” in Proceedings of the Symposium on Computer Animation, R. Boulic and D. K. Pai, Eds., p. 243, The Eurographics Association, Grenoble, France, August 2004. View at: Publisher Site  Google Scholar
 H. Hoppe, “Efficient implementation of progressive meshes,” Computers Graphics, vol. 22, no. 1, pp. 27–36, 1998. View at: Publisher Site  Google Scholar
 M. Garland and P. S. Heckbert, “Surface simplification using quadric error metrics,” in Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’97, pp. 209–216, ACM Press/AddisonWesley Publishing Co., New York, NY, USA, 1997. View at: Publisher Site  Google Scholar
 E. Landreneau and S. Schaefer, “Simplification of articulated meshes,” Computer Graphics Forum, vol. 28, no. 2, pp. 347–353, 2009. View at: Publisher Site  Google Scholar
 A. Willmott, “Rapid simplification of multiattribute meshes,” in Proceedings of the ACM SIGGRAPH Symposium on High Performance Graphics, HPG ’11, p. 151, ACM, New York, NY, USA, August 2011. View at: Publisher Site  Google Scholar
 W.W. Feng, B.U. Kim, Y. Yu, L. Peng, and J. Hart, “Featurepreserving triangular geometry images for levelofdetail representation of static and skinned meshes,” ACM Transactions on Graphics (TOG), vol. 29, no. 2, article no. 11, 2010. View at: Publisher Site  Google Scholar
 D. P. Savoy, M. C. Cabral, and M. K. Zuffo, “Crowd simulation rendering for web,” in Proceedings of the 20th International Conference on 3D Web Technology, Web3D ’15, pp. 159160, ACM, New York, NY, USA, June 2015. View at: Publisher Site  Google Scholar
 F. Tecchia, C. Loscos, and Y. Chrysanthou, “Real time rendering of populated urban environments,” Tech. Rep., ACM SIGGRAPH Technical Sketch, 2001. View at: Google Scholar
 J. Barczak, N. Tatarchuk, and C. Oat, “GPUbased scene management for rendering large crowds,” ACM SIGGRAPH Asia Sketches, vol. 2, no. 2, 2008. View at: Google Scholar
 B. Hernández and I. Rudomin, “A rendering pipeline for realtime crowds,” GPU Pro, vol. 2, pp. 369–383, 2016. View at: Google Scholar
 J. Zelsnack, “GLSL pseudoinstancing,” Tech. Rep., 2004. View at: Google Scholar
 F. Carucci, “Inside geometry instancing,” GPU Gems, vol. 2, pp. 47–67, 2005. View at: Google Scholar
 G. Ashraf and J. Zhou, “Hardware accelerated skin deformation for animated crowds,” in Proceedings of the 13th International Conference on Multimedia Modeling  Volume Part II, MMM07, pp. 226–237, SpringerVerlag, Berlin, Germany, 2006. View at: Publisher Site  Google Scholar
 F. Klein, T. Spieldenner, K. Sons, and P. Slusallek, “Configurable instances of 3D models for declarative 3D in the web,” in Proceedings of the Nineteenth International ACM Conference, pp. 71–79, ACM, New York, NY, USA, August 2014. View at: Publisher Site  Google Scholar
 C. Peng and Y. Cao, “Parallel LOD for CAD model rendering with effective GPU memory usage,” ComputerAided Design and Applications, vol. 13, no. 2, pp. 173–183, 2016. View at: Publisher Site  Google Scholar
 T. A. Funkhouser and C. H. Séquin, “Adaptive display algorithm for interactive frame rates during visualization of complex virtual environments,” in Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’93, pp. 247–254, ACM, New York, NY, USA, 1993. View at: Publisher Site  Google Scholar
 C. Peng and Y. Cao, “A GPUbased approach for massive model rendering with frametoframe coherence,” Computer Graphics Forum, vol. 31, no. 2, pp. 393–402, 2012. View at: Publisher Site  Google Scholar
 N. Bell and J. Hoberock, “Thrust: a productivityoriented library for CUDA,” in GPU Computing Gems Jade Edition, Applications of GPU Computing Series, pp. 359–371, Morgan Kaufmann, Boston, Mass, USA, 2012. View at: Google Scholar
 E. Millán and I. Rudomn, “Impostors, pseudoinstancing and image maps for gpu crowd rendering,” Iranian Journal of Veterinary Research, vol. 6, no. 1, pp. 35–44, 2007. View at: Google Scholar
 L. Toledo, O. De Gyves, and I. Rudomín, “Hierarchical level of detail for varied animated crowds,” The Visual Computer, vol. 30, no. 68, pp. 949–961, 2014. View at: Publisher Site  Google Scholar
 L. Lyu, J. Zhang, and M. Fan, “An efficiency control method based on SFSM for massive crowd rendering,” Advances in Multimedia, vol. 2018, pp. 1–10, 2018. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2019 Yangzi Dong and Chao Peng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.