Capturing and reconstructing a human actor's motion is important for filmmaking and gaming. Currently, motion capture systems with static cameras are used for pixel-level high-fidelity reconstructions. Such setups are costly, require installation and calibration and, more importantly, confine the user to a predetermined area. In this work, we present a drone-based motion capture system that can alleviate these limitations. We present a complete system implementation and study view planning which is critical for achieving high-quality reconstructions. The main challenge for view planning for a drone-based capture system is that it needs to be performed during motion capture. To address this challenge, we introduce simple geometric primitives and show that they can be used for view planning. Specifically, we introduce Pixel-Per-Area (PPA) as a reconstruction quality proxy and plan views by maximizing the PPA of the faces of a simple geometric shape representing the actor. Through experiments in simulation, we show that PPA is highly correlated with reconstruction quality. We also conduct real-world experiments showing that our system can produce dynamic 3D reconstructions of good quality. We share our code for the simulation experiments in the link: https://github.com/Qingyuan-Jiang/view_planning_3dhuman

翻译：捕捉并重建人体运动对于电影制作和游戏领域至关重要。当前基于静态摄像机的运动捕捉系统可实现像素级高保真重建，但此类设备成本高昂、需要安装校准，且更关键的是，它将使用者限制在预定区域内。本研究提出一种基于无人机的运动捕捉系统以突破这些局限。我们实现了完整系统方案，并重点研究了实现高质量重建的关键技术——视角规划。无人机捕捉系统的视角规划面临的核心挑战在于需在运动捕捉过程中同步完成。为应对这一挑战，我们引入简单的几何基元，并论证其可用于视角规划。具体而言，我们提出像素面积比（PPA）作为重建质量代理指标，通过最大化表征演员简单几何体各面的PPA值来规划视角。仿真实验表明PPA与重建质量具有高度相关性。真实场景实验也证明本系统可生成质量良好的动态三维重建。仿真实验代码已开源：https://github.com/Qingyuan-Jiang/view_planning_3dhuman