arXiv:2607. 15868v1 Announce Type: cross Abstract: Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied AI and VR/AR.
arXiv:2607. 09780v1 Announce Type: cross Abstract: The modern-day surge in popularity of wearable devices poses a fundamentally unique motion capture problem: reconstructing full-body movement from any set of sensing hardware worn at a given moment.
MEgoVista is an offline pipeline that converts a single unprepared egocentric video into metric two‑hand and head motion within a gravity‑aligned world frame. It uniquely reconstructs motion in environments beyond studio volumes, uses calibrated stereo for absolute scale, and evaluates its outputs against independent optical capture to audit accuracy. The system thus expands the settings where high‑fidelity hand‑motion labels can be generated from natural, head‑worn recordings.
arXiv:2608.29927v1 Announce Type: new Abstract: We address the problem of 3D body pose estimation of multiple interacting people from their egocentric views with centralized coordination. Each indivi...
arXiv:2609.01276v1 Announce Type: new Abstract: Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Exis...
Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment. A holistic and efficient multimodal model capable of reconstructing this 4D representation is therefore highly desirable.
arXiv:2607. 17790v1 Announce Type: cross Abstract: Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment.
Ego-Exo4D is a large-scale dataset that offers synchronized egocentric and multi-view exocentric video for applications such as skill learning, procedural activity understanding, and embodied AI. The original dataset only includes sparse 3D human pose annotations, making dense motion reconstruction challenging. To address this, the authors introduce Ego-Exo4D-HM, a new dataset containing 4D human motion reconstructions for the Ego-Exo4D captures, along with a reconstruction pipeline and accompanying code and documentation.
arXiv:2604. 01001v2 Announce Type: replace-cross Abstract: We introduce EgoSim, a closed-loop egocentric world simulator that generates spatially consistent interaction videos and persistently updates the underlying 3D scene state for continuous simulation.
arXiv:2606. 06872v1 Announce Type: cross Abstract: Estimating hand-surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis.
arXiv:2602. 23694v3 Announce Type: replace-cross Abstract: Human operators are still frequently exposed to hazardous environments such as disaster zones and industrial facilities, where intuitive and reliable teleoperation of mobile robots and Unmanned Aerial Vehicles (UAVs) is essential.
arXiv:2609.08636v1 Announce Type: cross Abstract: Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize...
EventEgoHands++ is a new framework for reconstructing 3D hand meshes from egocentric event-based cameras. It introduces a Hand Detector that provides instance-level bounding boxes and masks for left and right hands, and an Adaptive Attention module that uses these detections to model spatial relationships and interactions. The authors extend the synthetic N-HOT3D dataset and create EEH‑R, a large real-world event-based egocentric hand dataset with about 1 million annotated frames, and show that their method outperforms existing baselines on both synthetic and real data.