A state-of-the-art multi-view 3D tracking system from Meta Facebook Research optimized for egocentric hand-object interactions on XR devices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hot3d
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install hot3d using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is HOT3D: Multi-View 3D Hand & Object Tracking?
HOT3D is a comprehensive tracking framework designed for Meta Aria smart glasses and Quest VR headsets. As a core component of the Openclaw Skills ecosystem, it provides precise 3D world coordinates for 21-keypoint hand poses and 6DOF object tracking during complex manipulations. Unlike standard single-view solutions, HOT3D leverages multi-view egocentric data to achieve millimeter-level accuracy in 3D space.
This skill enables developers to reconstruct hand meshes and align object models within a synchronized multi-camera environment. By utilizing this Openclaw Skills entry, researchers and developers can access robust visualization tools for 3D skeleton projections and trajectory analysis, making it the definitive choice for high-fidelity XR interaction development.
HOT3D: Multi-View 3D Hand & Object Tracking Use Cases
- Implementing high-precision hand-object interaction analysis in XR environments.
- Research-grade benchmarking for 3D hand pose estimation algorithms.
- Developing applications for Meta Aria smart glasses and Quest headsets requiring world-coordinate tracking.
- Analyzing contact points and grasp types in daily activity sequences.
- Creating rich 3D overlays and skeletal visualizations on multi-view video streams.
How HOT3D: Multi-View 3D Hand & Object Tracking Works
- The system ingests multi-view RGB-D video streams synchronized across 3-5 cameras.
- Keypoint detection identifies 21 specific 3D hand joints with confidence scoring for each view.
- Object pose estimation calculates the 3D position and orientation (quaternion/rotation) of manipulated items.
- Hand-object interaction logic detects contact points and classifies manipulation phases.
- The final output generates 3D coordinates in a unified world frame, suitable for rendering meshes or skeletal overlays.
HOT3D: Multi-View 3D Hand & Object Tracking Setup
To integrate this Openclaw Skills module, follow these installation steps:
# Clone the HOT3D repository
git clone https://github.com/facebookresearch/hot3d.git
cd hot3d
# Install required dependencies including PyTorch3D and Open3D
pip install -r requirements.txt
# Note: Accessing the full dataset requires registration at the official project page
HOT3D: Multi-View 3D Hand & Object Tracking Data Schema & Taxonomy
HOT3D organizes tracking data through a structured frame-based taxonomy:
| Data Type | Description | Format |
|---|---|---|
| Hand Pose | 21-keypoint 3D joints (Left/Right) | 21x3 Array (x, y, z) |
| Object Pose | 6DOF position and rotation matrix | [x, y, z] + 3x3 Matrix |
| Metadata | Camera intrinsics and confidence scores | JSON / Dict |
| Visuals | 3D meshes and skeletal projections | OBJ / PNG Overlay |
HOT3D: Multi-View 3D Hand & Object Tracking Advanced Features
- Multi-agent support for tracking simultaneous interactions across different users.
- Temporal consistency algorithms to maintain tracking stability across high-frequency frames (30-60 Hz).
- Integration hooks for Aria SDK and OpenXR platforms.
- Automated force estimation capabilities when combined with auxiliary sensor data.
- Comprehensive evaluation suite via the hand-tracking-toolkit for metric analysis.
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