arXiv Computer Vision
Everybody Tracking Every Body
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arXiv Computer Vision
Sep 2
Seeing the World and the Self from Egocentric Video
arXiv:2609.01276v1 Announce Type: new Abstract: Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Exis...
arXiv AI
Jul 20
EgoExoMoCap: Distributed Ego-Exo Human Motion Capture
arXiv:2607. 15868v1 Announce Type: cross Abstract: Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied AI and VR/AR.
arXiv Computer Vision
Sep 25
Ego-Exo4D Human Meshes Dataset: 4D Human Motion Reconstruction for Ego-Exo Captures
Ego-Exo4D is a large-scale dataset that offers synchronized egocentric and multi-view exocentric video for applications such as skill learning, procedural activity understanding, and embodied AI. The original dataset only includes sparse 3D human pose annotations, making dense motion reconstruction challenging. To address this, the authors introduce Ego-Exo4D-HM, a new dataset containing 4D human motion reconstructions for the Ego-Exo4D captures, along with a reconstruction pipeline and accompanying code and documentation.
arXiv Computer Vision
Sep 30
HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstruction
arXiv:2603.12789v3 Announce Type: replace Abstract: Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independ...
arXiv AI
Sep 10
From Where to How: Continuous 4D Interaction Forecasting from Egocentric Video
arXiv:2609.08636v1 Announce Type: cross Abstract: Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize...
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.