Fast and Accurate Hand Estimation (FAH) is a new family of monocular hand mesh estimators that directly predict 3D joints and surface points, then fit a mesh model such as MANO or MHR-Hand. Built on SAM 3D Body’s hand branch, FAH uses fine‑tuning and feature distillation to produce accurate 2D and 3D keypoints, optional anatomical part segmentation, and contact estimation without requiring object geometry. The models achieve state‑of‑the‑art image‑space alignment, hand geometry, contact estimation, and world‑space tracking, running at 65 fps (ViT‑L) or 125 fps (ViT‑S) on an RTX 4070 Ti SUPER.
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:2610.20124v1 Announce Type: new Abstract: Human mesh recovery (HMR) provides a geometric foundation for understanding human motion, but accurately reconstructing both body motion and fine hand...
arXiv:2605.20992v4 Announce Type: replace Abstract: We ask whether everyday open-world monocular videos can be turned into reusable 4D interaction primitives: articulated hand motion, object shape wi...
arXiv:2608.28386v1 Announce Type: new Abstract: Existing monocular full-body 3D human-object interaction (HOI) methods do not combine explicit finger-level grasp optimization with category-agnostic o...
arXiv:2609.24424v1 Announce Type: new Abstract: Monocular RGB-based hand pose estimation has emerged as a critical research frontier in computer vision. The local hand pose estimation methods predict...
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...
arXiv:2608. 20093v1 Announce Type: new Abstract: In this work, we present HandMvNet, one of the first real-time method designed to estimate 3D hand motion and shape from multi-view camera images.
HOPE is a framework that estimates hand‑object pressure from monocular videos by treating pressure estimation as a hand‑centric video prediction task. It lifts diverse pressure and contact annotations into a shared hand vertex space and uses a vertex‑anchored video transformer with a contact‑gated pressure head to predict temporally evolving per‑vertex normal pressure and contact. Experiments on multiple datasets show that HOPE generalizes to bare‑hand egocentric and in‑the‑wild videos, outperforming baselines that rely only on contact or planar pressure.
Estimating physical pressure from vision is essential for understanding contact-rich hand-object interaction. However, prior vision-based pressure estimation methods are largely limited to planar surfaces and single image input, making them difficult to apply to dynamic hand-object interaction with diverse objects.
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...
EgoHSI3R is a unified feed‑forward framework that reconstructs 4D hand‑scene interactions from egocentric monocular video. It jointly estimates camera motion, dense scene geometry, world‑space bilateral MANO hands, and per‑vertex hand contact by maintaining a recurrent scene representation and using hand‑centric prompts and MANO vertex queries. The method achieves the lowest world‑space hand joint error on ARCTIC while offering faster inference than existing pipelines.
arXiv:2606. 25619v2 Announce Type: replace Abstract: In this paper, we present ScaleHP, a unified framework that explicitly represents per-instance metric scale to resolve the coupled errors in calibrated camera-space hand pose estimation.