activity
20242026
most citedIntroducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking

2 citations · 4 across the 17 of their papers we have counts for

collaborators

19 papers

cs.CV2026

SuperMemory-VQA: An Egocentric Visual Question-Answering Benchmark for Long-Horizon Memory

Samiul Alam, Shakhrul Iman Siam, Michael J. Proulx +4

AI glasses present a compelling platform for AI agents to serve as personalized memory assistants. To be genuinely useful, such systems must move beyond short-term video comprehens…

cs.CV2026

EC: Video Generation with 3D Environmental Memory and Ego-Exo Human Pose Control

Qiao Gu, Lingni Ma, Adam W Harley +3

Controllable and physically grounded egocentric video generation is essential for embodied agents to reason about how their own and others' actions manifest and change the world. C…

cs.CV2026

LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World

Nan Yang, Julian Straub, Fan Zhang +3

Tracking 3D human motion from egocentric multi-camera headset is challenged by severe egomotion, partial visibility or occlusions and lack of training data. Existing methods design…

cs.CV2026

Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D

Daniel DeTone, Tianwei Shen, Fan Zhang +4

Detecting and localizing objects in space is a fundamental computer vision problem. While much progress has been made to solve 2D object detection, 3D object localization is much l…

cs.RO2026

EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World

Ryan Punamiya, Simar Kareer, Zeyi Liu +37

Robot learning increasingly depends on large and diverse data, yet robot data collection remains expensive and difficult to scale. Egocentric human data offer a promising alternati…

cs.CV2026

JRM: Joint Reconstruction Model for Multiple Objects without Alignment

Qirui Wu, Yawar Siddiqui, Duncan Frost +6

Object-centric reconstruction seeks to recover the 3D structure of a scene through composition of independent objects. While this independence can simplify modeling, it discards st…

cs.CV2026

NymeriaPlus: Enriching Nymeria Dataset with Additional Annotations and Data

Daniel DeTone, Federica Bogo, Eric-Tuan Le +7

The Nymeria Dataset, released in 2024, is a large-scale collection of in-the-wild human activities captured with multiple egocentric wearable devices that are spatially localized a…

cs.CV2026

ShapeR: Robust Conditional 3D Shape Generation from Casual Captures

Yawar Siddiqui, Duncan Frost, Samir Aroudj +9

Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely…

cs.AR2025

Full System Architecture Modeling for Wearable Egocentric Contextual AI

Vincent T. Lee, Tanfer Alan, Sung Kim +6

The next generation of human-oriented computing will require always-on, spatially-aware wearable devices to capture egocentric vision and functional primitives (e.g., Where am I? W…

cs.CV2025

Aria Gen 2 Pilot Dataset

Chen Kong, James Fort, Aria Kang +20

The Aria Gen 2 Pilot Dataset (A2PD) is an egocentric multimodal open dataset captured using the state-of-the-art Aria Gen 2 glasses. To facilitate timely access, A2PD is released i…

cs.CV2025

Benchmarking Egocentric Visual-Inertial SLAM at City Scale

Anusha Krishnan, Shaohui Liu, Paul-Edouard Sarlin +6

Precise 6-DoF simultaneous localization and mapping (SLAM) from onboard sensors is critical for wearable devices capturing egocentric data, which exhibits specific challenges, such…

cs.CV2025

Sonata: Self-Supervised Learning of Reliable Point Representations

Xiaoyang Wu, Daniel DeTone, Duncan Frost +7

In this paper, we question whether we have a reliable self-supervised point cloud model that can be used for diverse 3D tasks via simple linear probing, even with limited data and…

cs.CV2025

Human-in-the-Loop Local Corrections of 3D Scene Layouts via Infilling

Christopher Xie, Armen Avetisyan, Henry Howard-Jenkins +5

We present a novel human-in-the-loop approach to estimate 3D scene layout that uses human feedback from an egocentric standpoint. We study this approach through introduction of a n…

cs.CV2024★ 1 cited

HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos

Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon +11

We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (3.7M+ images) of recordings that feature 19 sub…

cs.CV2024

EgoLM: Multi-Modal Language Model of Egocentric Motions

Fangzhou Hong, Vladimir Guzov, Hyo Jin Kim +4

As the prevalence of wearable devices, learning egocentric motions becomes essential to develop contextual AI. In this work, we present EgoLM, a versatile framework that tracks and…

cs.CV2024

HMD^2: Environment-aware Motion Generation from Single Egocentric Head-Mounted Device

Vladimir Guzov, Yifeng Jiang, Fangzhou Hong +5

This paper investigates the generation of realistic full-body human motion using a single head-mounted device with an outward-facing color camera and the ability to perform visual…

cs.CV2024★ 2 cited

Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking

Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon +9

We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monoch…

cs.CV2024

Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild

Lingni Ma, Yuting Ye, Fangzhou Hong +14

We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multiple multimodal egocentric devices. The dataset comes with a) fu…

cs.CV2024★ 1 cited

SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model

Armen Avetisyan, Christopher Xie, Henry Howard-Jenkins +11

We introduce SceneScript, a method that directly produces full scene models as a sequence of structured language commands using an autoregressive, token-based approach. Our propose…