Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/VRW55335.2022.00213 | 2022 IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES ABSTRACTS AND WORKSHOPS (VRW 2022) |
Keywords | DocType | Citations |
Computing methodologies, Artificial intelligence, Computer vision, Motion capture, Computing methodologies, Artificial intelligence, Computer vision, 3D imaging | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georgios Albanis | 1 | 0 | 0.68 |
| Anargyros Chatzitofis | 2 | 0 | 0.34 |
| Spyridon Thermos | 3 | 0 | 0.68 |
| Nikolaos Zioulis | 4 | 34 | 10.15 |
| Kostas Kolomvatsos | 5 | 299 | 30.48 |