arXiv AI
A Head Camera Is (Almost) All You Need: An Electronics-Free UMI Glove Labelled and Observed by One Moving Camera
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arXiv Computer Vision
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MEgoVista: Multi-view Ego-aware Motion Estimation for Metric 4D Hands and Head in the Wild
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 AI
Oct 5
EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras
arXiv:2610.03710v1 Announce Type: cross Abstract: Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. E...
arXiv Computer Vision
Sep 18
INSPECT: Learning Robot View Selection from Assistant Use
INSPECT is a system that learns how a robot should choose its camera view during assembly inspection by observing a smart‑glasses assistant that answers part queries and guides the user. It uses techniques such as Presence‑Invariant TwinSwap for object evidence calibration, claim‑indexed supervision to separate evidence needs from camera changes, and object‑centered calibration to adapt view preferences to robot poses. In experiments on gearbox assemblies and angle‑grinder recordings, INSPECT outperforms other non‑oracle policies, improving view utility and decision accuracy.
arXiv Machine Learning
Oct 7
EVFormer: An Egocentric Vision-EMG Bidirectional Attention Model for Bimanual Hand Pose Estimation
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Hugging Face Trending Papers
Sep 28
One Sensor, Whole Body - 3D Body Pose from a Single Consumer Earbud IMU
The study investigates how well a single consumer earbud IMU can estimate 3D body pose and whether adding foot IMUs improves accuracy. Using a multimodal capture pipeline with RGB‑D video, an AirPods head IMU, and Striv insole IMUs, the authors benchmark pose estimation across various motions and train recurrent models (IMUPoser and MobilePoser). Results show that a head IMU alone achieves 79.0 mm rigid‑MPJPE and 0.809 macro‑F1 for foot contact, while adding foot IMUs does not significantly improve pose and can even degrade performance due to insole orientation errors.
arXiv Machine Learning
Oct 8
TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning
arXiv:2605.07943v2 Announce Type: replace-cross Abstract: Active vision -- where a policy controls its own gaze during manipulation -- has emerged as a key capability for imitation learning, with mul...