CHS: Small: A Perceptual-based Approach to improve Synthetic Crowds

原则上，人群模拟可以帮助设计师让建筑变得更好、更安全，因为他们可以探索人们在正常和紧急情况下如何通过建筑。然而，目前尚不清楚现有的模拟人群的工具是否反映了真人的运动，这让设计师对是否使用它们犹豫不决。该项目的目标是开发一种方法，允许人们直观地比较计算机生成的人群运动的真实感与真实的人群运动。这个想法是，模拟的人群看起来越真实，那么用于生成模拟的算法和模拟本身就越有可能在建筑设计中有用，并将受到建筑设计师的信任。该团队将通过以下方式解决这些问题：(1)处理建筑物中真实人群运动的视频以生成3D重建，然后(2)要求非专家和设施经理(A)对使用真实人群路径的平行视频与通过重建建筑的模拟人群路径的真实性进行评级，以及(B)对影响其评级的视频的各个方面进行评论。这些数据将有助于评估和改进未来人群模拟算法的质量；为此，该团队将发布视频、数据集、算法、实验工具和结果，以帮助这一领域和相关领域的其他研究人员。他们还将在旨在模拟和建模的大学课程中使用这些材料，并为初中生和高中生开发推广经验，为建筑设计师提供推广材料。为了生成实验材料，团队将首先处理从现有人群运动视频数据库中提取的人群视频，并在可能的情况下由团队捕获，以表示这些数据库中没有的条件。然后，他们将提取人们的初始位置和路径，使用Catmull-Rom样条线来补偿提取位置和建筑特征时的噪音以及视频之间的帧速率差异，以及创建设施的3D重建。为了生成模拟路径，他们将使用捕获的开始位置、时间和结束位置作为一套开源代理转向算法的输入，这些算法代表了各种模拟方法。为了从原始电影中删除可能影响判断的视觉线索，原始路径和模拟路径都将使用在群组模拟研究中常用的Unity 3D图形渲染和物理引擎生成。最后，该团队将开发一个界面，用于对视频对进行评级和注释(对于较长的视频，视频片段)以实现真实感。该界面将用于上述实验，在这些实验中，参与者比较真实渲染和模拟渲染，以及同一视频不同片段之间的比较，以收集有关影响真实感判断的因素的尽可能广泛的各种数据。

In principle, crowd simulations could help designers make buildings both nicer and safer by allowing them to explore how people would move through the building in both normal and emergency situations. However, it is unclear whether existing tools for simulating crowds reflect the motion of actual people, which makes designers hesitant to use them. This project's goal is to develop a method that allows people to visually compare the realism of computer-generated crowd movement to real crowd movement. The idea is that the more a simulated crowd looks as real as an actual crowd, then the more likely that the algorithm used to generate the simulation and the simulation itself will be useful in building design and will be trusted by building designers. The team will address these questions by (1) processing videos of real crowd motion in buildings to generate 3D reconstructions, then (2) asking both non-experts and facilities managers to (a) rate the realism of parallel videos that use the real crowd paths versus simulated crowd paths through the reconstructed building and (b) comment on aspects of the videos that affect their ratings. These data will be useful for both evaluating and improving the quality of future crowd simulation algorithms; to this end, the team will release the videos, datasets, algorithms, experimental tools, and results to help other researchers in this and related areas. They will also use the materials in college courses aimed at simulation and modeling, as well as developing outreach experiences for middle and high school students and outreach materials for building designers.To generate the experimental materials, the team will first process crowd videos drawn from existing crowd movement video databases when possible and captured by the team when needed to represent conditions not available in those databases. They will then extract people's initial locations and paths, using Catmull-Rom splines to compensate for noise in the extraction of location and building features and variations in frame rates between videos, as well as creating a 3D reconstruction of the facility. For generating simulated paths, they will use the captured starting location, time, and ending location as input to a suite of open source agent-steering algorithms that represent a wide variety of simulation approaches. To remove visual cues from the original films that might influence judgments, both the original paths and the simulated paths will be generated using the Unity 3D graphics rendering and physics engine, which is commonly used in crowd simulation research. Finally, the team will develop an interface for rating and annotating pairs of videos (and, for longer videos, video segments) for realism. This interface will be used in the experiments described above in which participants compare real and simulated renderings, as well as comparisons between different segments of the same video, to collect as wide a variety of data as possible on factors that affect realism judgments.