Towards Scalable and Real-time Markerless Motion Capture

Georgios Albanis, Anargyros Chatzitofis, Spyridon Thermos, Nikolaos Zioulis, Kostas Kolomvatsos

Abstract: Human motion capture and perception without the need for complex systems with specialized cameras or wearable equipment is the holy grail for many human-centric applications. Here, we present a scalable markerless motion capture method that estimates 3D human poses in real-time using low-cost hardware. We do so by replacing the inefficient 3D joint reconstruction techniques, such as learnable triangulation and feature splatting, with a novel uncertainty-driven approach that exploits the available depth information and the edge sensors’ spatial alignment to fuse the per viewpoint estimates into final 3D joint positions.