Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking
HOT3D dataset offers multi-view images and annotations for 3D hand and object tracking, boosting research.
Key Findings
Methodology
The HOT3D dataset uses Meta's Project Aria and Quest 3 devices to record interactions of 19 participants with 33 objects. It includes multi-view RGB/monochrome image streams, eye gaze data, and scene point clouds. Hand annotations are in UmeTrack and MANO formats, and objects are represented by PBR-material 3D meshes. Ground-truth poses were obtained using a professional motion-capture system.
Key Results
- The dataset provides 833 minutes of recordings, over 3.7 million images, covering kitchen, office, and living room scenarios, supporting various hand-object interaction research.
- With synchronized multi-view image streams, it supports training and evaluating model-based and model-free 3D hand-object tracking methods.
- Offers high-quality 3D models and precise ground-truth annotations, facilitating the synthesis of photorealistic training images.
Significance
The HOT3D dataset fills a gap in existing datasets for multi-view, photorealistic 3D hand-object interaction scenarios. It provides researchers with rich training and testing resources, advancing the field of hand-object interaction. Through public challenges, it fosters collaboration and innovation in academia and industry.
Technical Contribution
HOT3D excels in providing synchronized multi-view image streams and high-quality 3D models. It supports complex hand-object interaction scenarios with precise ground-truth annotations, applicable to various computer vision tasks.
Novelty
HOT3D is the first dataset to offer multi-view, photorealistic 3D hand-object interaction data, using advanced motion-capture technology and high-resolution 3D scanners to provide unprecedented detail and accuracy.
Limitations
- The dataset primarily focuses on indoor scenes, lacking data for outdoor and dynamic environments.
- Although it provides multi-view data, the perspective is limited by the head-mounted devices.
- Dynamic interaction scenarios between hands and objects are limited, which may affect research on complex interactions.
Future Work
Future research can expand to outdoor scenes, increase the complexity of dynamic interactions, and explore more application areas such as robotic learning and human-computer interaction.
AI Executive Summary
The HOT3D dataset is a multi-view dataset for 3D hand and object tracking, aiming to advance hand-object interaction research. Existing methods lack the precision and speed to support complex applications, and HOT3D fills this gap by providing high-quality multi-view images and precise ground-truth annotations.
The dataset is recorded using Meta's Project Aria and Quest 3 devices, featuring interactions of 19 participants with 33 objects across kitchen, office, and living room scenarios. It offers over 3.7 million images, supporting various computer vision tasks such as 3D reconstruction and hand-object interaction detection.
Through public challenges, HOT3D fosters collaboration between academia and industry, driving innovation in hand-object interaction. Future research can further expand the dataset's application scope, exploring more scenarios and interaction modes.
Deep Analysis
Background
Research on hand-object interaction is significant in computer vision, involving human-computer interaction, augmented reality, and robotics. Existing datasets often focus on single-view or static scenes, lacking support for multi-view and dynamic interactions.
Core Problem
Existing methods perform poorly in complex hand-object interaction scenarios, especially in multi-view and dynamic environments. Accurate 3D pose estimation and high-quality training data are crucial for achieving better performance.
Innovation
HOT3D provides synchronized multi-view image streams and high-quality 3D models, supporting complex hand-object interaction research. It uses advanced motion-capture technology to provide precise ground-truth annotations.
Methodology
- �� Use Meta's Project Aria and Quest 3 devices to record data
- �� Provide synchronized multi-view image streams for various computer vision tasks
- �� Use motion-capture system to obtain precise ground-truth annotations
- �� Offer high-quality 3D models for photorealistic training image synthesis
Experiments
The dataset includes 833 minutes of recordings, over 3.7 million images. Experimental design includes the collection of synchronized multi-view image streams and the generation of high-quality 3D models, supporting training and evaluation of various computer vision tasks.
Results
The HOT3D dataset provides rich training and testing resources, supporting various hand-object interaction research. With synchronized multi-view image streams and high-quality 3D models, it facilitates the synthesis of photorealistic training images.
Applications
HOT3D can be used in augmented reality, virtual reality, and robotic learning, supporting complex hand-object interaction scenarios and applications.
Limitations & Outlook
The dataset primarily focuses on indoor scenes, lacking data for outdoor and dynamic environments. Future research can expand to more scenarios and interaction modes.
Plain Language Accessible to non-experts
Imagine you're in a kitchen, holding a cup and a spoon. You need to observe them from different angles to better understand their shapes and positions. The HOT3D dataset is like a virtual kitchen, providing multi-view images that allow computers to understand hand and object interactions like humans do. It's like giving computers a pair of smart glasses, enabling them to see everything you see and understand how you interact with objects.
ELI14 Explained like you're 14
Imagine you're playing a virtual reality game where you need to grab and manipulate various objects with your hands. The HOT3D dataset is like a super detailed game guide for computers, telling them how to track your hands and objects in 3D space. With this data, computers can better understand your actions in the game and provide a smoother gaming experience. Isn't that cool?
Glossary
Project Aria
A lightweight AR/AI glasses prototype developed by Meta for recording multi-view data.
Used to record multi-view image streams in the HOT3D dataset.
Quest 3
A VR headset produced by Meta, widely used in virtual reality applications.
Used to record multi-view image streams in the HOT3D dataset.
UmeTrack
A format for hand pose annotations, providing high-precision hand pose information.
Used for hand annotations in the HOT3D dataset.
MANO
A standard hand model format for representing hand poses and shapes.
Used for hand annotations in the HOT3D dataset.
PBR Materials
Physically-based rendering materials that provide realistic material effects.
Used in 3D object models in the HOT3D dataset.
Open Questions Unanswered questions from this research
- 1 How to achieve high-precision hand-object tracking in outdoor and dynamic environments? Existing datasets mainly focus on indoor scenes.
- 2 How to improve dataset diversity, especially in complex interaction scenarios?
- 3 How to effectively utilize multi-view data for real-time hand-object interaction detection?
Applications
Immediate Applications
Augmented Reality Applications
Develop more precise AR applications using the HOT3D dataset, enabling gesture control and object interaction.
Virtual Reality Gaming
Enhance hand-object interaction experiences in VR games using multi-view images from the dataset.
Long-term Vision
Robotic Learning
Improve robots' autonomous learning capabilities in complex tasks by learning human hand-object interactions.
Abstract
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/monochrome image streams showing 19 subjects interacting with 33 diverse rigid objects, multi-modal signals such as eye gaze or scene point clouds, as well as comprehensive ground truth annotations including 3D poses of objects, hands, and cameras, and 3D models of hands and objects. In addition to simple pick-up/observe/put-down actions, HOT3D contains scenarios resembling typical actions in a kitchen, office, and living room environment. The dataset is recorded by two head-mounted devices from Meta: Project Aria, a research prototype of light-weight AR/AI glasses, and Quest 3, a production VR headset sold in millions of units. Ground-truth poses were obtained by a professional motion-capture system using small optical markers attached to hands and objects. Hand annotations are provided in the UmeTrack and MANO formats and objects are represented by 3D meshes with PBR materials obtained by an in-house scanner. We aim to accelerate research on egocentric hand-object interaction by making the HOT3D dataset publicly available and by co-organizing public challenges on the dataset at ECCV 2024. The dataset can be downloaded from the project website: https://facebookresearch.github.io/hot3d/.