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:2606. 09243v1 Announce Type: cross Abstract: Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware.
TouchSight is a monocular egocentric vision framework that predicts dense full-hand contact forces without tactile sensors. It uses 500 hours of pressure-glove data and a 20-hour TwinTouch-20H dataset where generative models render gloved recordings as bare-hand videos, bridging the appearance gap. The system outperforms previous methods on OakInk2, generalizes to unseen natural bare-hand egocentric videos, and improves as glove supervision increases.
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.
TouchSight is a monocular egocentric vision framework that predicts dense full-hand contact forces from video. It uses 500 hours of pressure‑glove recordings and hand‑object interaction data, and introduces TwinTouch‑20H, a dataset of 20 hours of paired visual data where generative models render gloved recordings as bare‑hand observations while preserving tactile labels. The system outperforms prior methods on OakInk2, generalizes qualitatively to natural bare‑hand egocentric videos from unseen datasets, and improves consistently as glove supervision scales.
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:2608.13014v2 Announce Type: replace Abstract: Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet...
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.
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: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...
FlowTouch is a model that predicts tactile sensor outputs from visual data in a view‑invariant manner by leveraging an object’s local 3D mesh to encode rich information while abstracting scene‑dependent details. It integrates scene reconstruction with Flow Matching‑based image generation, enabling it to bridge the sim‑to‑real gap and generalize to new sensor instances. The predicted tactile images can be used for downstream tasks such as grasp stability prediction.
arXiv:2606.19156v2 Announce Type: replace Abstract: Dynamic 3D hand reconstruction from egocentric videos is essential for next-generation computing platforms such as AR/VR and AI glasses. Despite it...