arXiv Computer Vision
EgoHSI3R: Online 4D Hand-Scene Interaction Reconstruction from Egocentric Video
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arXiv Computer Vision
Oct 8
Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos
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...
arXiv Computer Vision
Sep 7
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 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.
arXiv Computer Vision
Sep 16
EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Dataset
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.
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...