用3D高斯点云实现实时逼真人群渲染
CrowdSplat: Exploring Gaussian Splatting For Crowd Rendering
- 用单目视频提取人物姿态与服饰，构建3D高斯点云模型
- 结合层级细节渲染，在1080p下实现60帧/秒的流畅效果
- 适合游戏、影视等需要大规模动态人群的实时场景
我们提出CrowdSplat，一种基于3D高斯点云的实时高质量人群渲染方法。该方法利用3D高斯函数表示不同姿态与着装的动画人物，其数据源自单目视频。通过引入层级细节（LoD）渲染策略，优化了计算效率与视觉质量。整体框架分为两个阶段：（1）角色重建，（2）人群合成。同时针对GPU内存使用进行优化，提升可扩展性。定量与定性评估表明，CrowdSplat在渲染质量、内存效率和计算性能方面均表现良好。实验验证了其在实时应用中实现动态逼真人群模拟的可行性。
原文摘要 · Abstract (English)
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 the.se experiments, we demonstrate that CrowdSplat is a viable solution for dynamic, realistic crowd simulation in real-time applications.
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