arXiv Computer Vision
EgoM2P: Egocentric Multimodal Multitask Pretraining
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arXiv AI
Jul 21
ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video
arXiv:2607. 17790v1 Announce Type: cross Abstract: Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment.
Hugging Face Trending Papers
Jul 20
ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video
Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment. A holistic and efficient multimodal model capable of reconstructing this 4D representation is therefore highly desirable.
arXiv AI
2d ago
EgoGen: An Egocentric Synthetic Data Generator
EgoGen is a synthetic data generator designed for egocentric perception tasks, producing accurate ground‑truth training data by simulating natural human movements that steer virtual cameras. Its core is a human motion synthesis model that uses egocentric visual inputs to sense 3D environments, combined with collision‑avoiding motion primitives and a two‑stage reinforcement learning approach for closed‑loop perception and movement. The system eliminates the need for pre‑defined global paths, works in dynamic settings, and supports tasks such as mapping, localization, camera tracking, and human mesh recovery from first‑person views.
arXiv AI
Jun 29
EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning
arXiv:2603. 09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-term physical consequences of actions from an egocentric viewpoint.
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
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...