arXiv Computer Vision
DewHand: Direct Metric World-Space Hand Motion Reconstruction from Egocentric Videos
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arXiv Computer Vision
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MINT: A Unified Model for World-Space Camera and Hand Motion Estimation from Scalable Egocentric Pipeline Supervision
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.
arXiv AI
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StableHand: Quality-Aware Flow Matching for World-Space Dual-Hand Motion Estimation from Egocentric Video
arXiv:2605.18553v2 Announce Type: replace-cross Abstract: Recovering world space 4D motion of two interacting hands from egocentric video is a fundamental capability for supervising robot policy lear...
arXiv Computer Vision
Sep 1
ACE-Ego-Hand: Repurposing Video Diffusion Models for Occlusion-Robust Egocentric 3D Hand Motion Recovery
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 Computer Vision
Aug 21
DreamHand: Repurposing Video Diffusion Models for Occlusion-Robust Egocentric 3D Hand Motion Recovery
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.
arXiv Computer Vision
Oct 2
EgoFound3R: End-to-End Egocentric Hand Reconstruction in World Space with Point-Wise Interaction Attributes
arXiv:2610.01210v1 Announce Type: new Abstract: Egocentric video has become a primary source of supervision for embodied models, and its value rests on recovering hand motion in world coordinates, wh...
arXiv Machine Learning
Oct 23
EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video
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.