We present CrowdSplat, a novel approach that leverages 3D Gaussian Splatting for real-time, high-quality crowd rendering. Our method utilizes 3D Gaussian functions to represent animated human characters in diverse poses and outfits, which are extracted from monocular videos. We integrate Level of Detail (LoD) rendering to optimize computational efficiency and quality. The CrowdSplat framework consists of two stages: (1) avatar reconstruction and (2) crowd synthesis. The framework is also optimized for GPU memory usage to enhance scalability. Quantitative and qualitative evaluations show that CrowdSplat achieves good levels of rendering quality, memory efficiency, and computational performance. Through these experiments, we demonstrate that CrowdSplat is a viable solution for dynamic, realistic crowd simulation in real-time applications.
我们提出了CrowdSplat，一种利用3D高斯点渲染进行实时高质量人群渲染的新方法。我们的方法利用3D高斯函数表示从单目视频中提取的各种姿势和服装的动画人物。我们集成了细节层次（LoD）渲染，以优化计算效率和质量。CrowdSplat框架分为两个阶段：（1）虚拟形象重建，（2）人群合成。该框架还针对GPU内存使用进行了优化，以增强可扩展性。定量和定性评估表明，CrowdSplat在渲染质量、内存效率和计算性能方面都取得了良好的水平。通过这些实验，我们展示了CrowdSplat在实时应用中实现动态、逼真的人群仿真的可行性。