EventEgo3D is a novel approach for real-time 3D human motion capture from egocentric event streams. The method introduces a new problem of 3D human motion capture using a monocular event camera with a fisheye lens. It proposes the first end-to-end trainable neural approach, EventEgo3D (EE3D), which enables high 3D reconstruction accuracy and real-time 3D pose update rates of 140Hz. EE3D is designed for lightweight processing and uses a compact head-mounted device with an event camera. The method also introduces a residual event propagation module that highlights human-generated events and provides reliable predictions even in the absence of motion. The paper addresses the challenges of designing an HMD with a monocular event camera and introduces a prototypical design of a compact HMD that can be worn by a human and used under fast motions. The method uses a lightweight neural network architecture operating on a suitable event stream representation (LNES) for real-time performance. EE3D encodes incoming events in the compact representation and decodes 2D heatmaps of the observed human joint locations. The lifting block then regresses the 3D human poses. The paper also proposes a real-world dataset, EE3D-R, which includes 3D ground-truth human poses and 2D event stream observations. This dataset allows for fine-tuning methods trained on the synthetic dataset and boosts the accuracy of egocentric event-based 3D pose estimation in real-world scenarios. The method is evaluated against existing approaches and demonstrates superior 3D reconstruction accuracy and real-time performance. The paper also discusses the challenges of using event cameras for 3D reconstruction and highlights the advantages of event-based methods, such as high temporal resolution and dynamic range. The proposed method is shown to be effective in handling complex motions and challenging scenarios, including fast-paced and jittery movements. The paper concludes that EventEgo3D is a promising approach for 3D human motion capture from egocentric event streams and that the use of event cameras in this setting offers many advantages. The method is well-suited for mobile devices and has the potential to be applied in various fields, including education, gaming, and personal assistance systems.EventEgo3D is a novel approach for real-time 3D human motion capture from egocentric event streams. The method introduces a new problem of 3D human motion capture using a monocular event camera with a fisheye lens. It proposes the first end-to-end trainable neural approach, EventEgo3D (EE3D), which enables high 3D reconstruction accuracy and real-time 3D pose update rates of 140Hz. EE3D is designed for lightweight processing and uses a compact head-mounted device with an event camera. The method also introduces a residual event propagation module that highlights human-generated events and provides reliable predictions even in the absence of motion. The paper addresses the challenges of designing an HMD with a monocular event camera and introduces a prototypical design of a compact HMD that can be worn by a human and used under fast motions. The method uses a lightweight neural network architecture operating on a suitable event stream representation (LNES) for real-time performance. EE3D encodes incoming events in the compact representation and decodes 2D heatmaps of the observed human joint locations. The lifting block then regresses the 3D human poses. The paper also proposes a real-world dataset, EE3D-R, which includes 3D ground-truth human poses and 2D event stream observations. This dataset allows for fine-tuning methods trained on the synthetic dataset and boosts the accuracy of egocentric event-based 3D pose estimation in real-world scenarios. The method is evaluated against existing approaches and demonstrates superior 3D reconstruction accuracy and real-time performance. The paper also discusses the challenges of using event cameras for 3D reconstruction and highlights the advantages of event-based methods, such as high temporal resolution and dynamic range. The proposed method is shown to be effective in handling complex motions and challenging scenarios, including fast-paced and jittery movements. The paper concludes that EventEgo3D is a promising approach for 3D human motion capture from egocentric event streams and that the use of event cameras in this setting offers many advantages. The method is well-suited for mobile devices and has the potential to be applied in various fields, including education, gaming, and personal assistance systems.