arXiv:2610.29936v1 Announce Type: new Abstract: Recovering metric world-space hand motion from egocentric video supports understanding human interactions and transferring them to embodied agents. Thr...
MINT is a foundation model that directly predicts world-space two-hand trajectories from egocentric RGB video, jointly estimating camera motion, hand states, and hand presence in a single spatiotemporal representation. It uses an open-source labeling pipeline, EGOPIPELINE, to generate large-scale pseudo-labels for pretraining, followed by fine-tuning on a small set of high-quality joint annotations. The model outperforms existing multi-stage approaches in accuracy and speed, and generalizes zero‑shot to unseen egocentric datasets.
Accurate monocular 4D hand reconstruction remains challenging. Per-frame discriminative regressors lack temporal context and often produce jittery predictions.
arXiv:2607. 11221v1 Announce Type: cross Abstract: Accurate monocular 4D hand reconstruction remains challenging.
arXiv:2610.09455v1 Announce Type: new Abstract: Recently, approaches that leverage human video datasets for robot policy training have become increasingly prevalent. However, most existing hand track...
arXiv:2608.20308v2 Announce Type: replace Abstract: Egocentric video offers scalable manipulation data for embodied AI, yet recovering metric 3D hand trajectories remains challenging due to severe ob...
arXiv:2608. 20308v1 Announce Type: new Abstract: Egocentric video offers scalable manipulation data for embodied AI, yet recovering metric 3D hand trajectories remains challenging due to severe object occlusion and frequent out-of-sight gaps.
The paper introduces JoHan, a generative framework that directly recovers 2D and 3D hand motion from video sequences without intermediate per‑frame pose predictions. By jointly learning temporal dynamics and 2D‑3D correspondence, JoHan generates aligned pose sequences that improve temporal consistency and enable accurate estimation of the hand’s global position and orientation relative to the camera. Experiments on challenging benchmarks show that JoHan achieves higher accuracy and faster performance, producing smoother hand‑motion dynamics while maintaining high per‑frame pose accuracy.
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.
ESTHER is an end‑to‑end model for egocentric stereo hand reconstruction that integrates stereo geometry, temporal reasoning, and a suitable output representation for wearable cameras. It is trained on pseudo‑labels from a calibrated pipeline and introduces the ESTHER3D benchmark, which pairs a large in‑the‑wild training set with a motion‑capture test set of true metric ground truth. Experiments demonstrate state‑of‑the‑art accuracy, strong generalization, and robustness to missing views, dropped frames, and extreme lighting or motion blur, while preserving metric scale even when reduced to a single monocular view.
arXiv:2610.27946v1 Announce Type: new Abstract: Egocentric action transfer aims to decouple the semantic action from egocentric observations and reproduce them in new visual contexts, enabling scalab...
EgoHand is a monocular 3D hand reconstruction model that sets new state‑of‑the‑art results on egocentric video. It is trained on a curated 14‑million‑image corpus that blends synthetic data, in‑the‑wild video, and multi‑camera motion capture to balance diversity and supervision quality. The model uses SOMA‑Hand as its parametric representation, processes 2D hand crops with a transformer encoder, and incorporates camera cues and an auxiliary 2D branch to enhance geometric consistency and image alignment, achieving low inference latency and strong out‑of‑domain generalization on benchmarks such as HOT3D, ARCTIC, and H2O.