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About this paper
Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon, Shreyas Hampali, Fan Zhang, Jade Fountain, Edward Miller, Selen Basol and 4 more
HOT3D, an egocentric dataset from Meta Reality Labs, provides high-fidelity 3D hand and object tracking ground truth using marker-based motion capture, collected across diverse dynamic interactions from AR/VR head-mounted devices. It includes multi-modal data and detailed 3D object models to accelerate research in egocentric perception.
Key insights
- High-fidelity 3D ground truth, obtained via marker-based motion capture, is critical for training and benchmarking robust egocentric hand-object tracking models.
- Data collected directly from AR/VR head-mounted devices in diverse, dynamic interaction scenarios is essential for developing models that generalize to real-world applications.
- Multi-modal signals (eye gaze, point clouds) and detailed 3D object models with PBR materials provide richer context for improving perception and enabling synthetic data generation.
Summary by alphaXiv.
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