Snap inc. (20240303926). HAND SURFACE NORMAL ESTIMATION simplified abstract
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HAND SURFACE NORMAL ESTIMATION
Organization Name
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HAND SURFACE NORMAL ESTIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303926 titled 'HAND SURFACE NORMAL ESTIMATION
Simplified Explanation: The patent application describes a system for enhancing images using hand surface normal estimation. This involves training a model with 3D hand models and corresponding normal data to estimate surface normals, which can then be used to augment hand image data.
- 3D hand models are generated using 3D data of hands in various positions.
- Target normal training data is created with normals of the 3D models and synthetic 2D image data.
- A normal estimation model is trained using the target normal training data and synthetic image training data.
- The normal estimation model is used in an interactive application to generate augmentations for hand image data.
Key Features and Innovation:
- Generation of 3D hand models for training.
- Creation of target normal training data with 3D model normals and synthetic 2D image data.
- Training a normal estimation model using the generated data.
- Application of normal estimation for image augmentation.
Potential Applications: This technology can be used in:
- Virtual reality and augmented reality applications.
- Medical imaging for hand analysis.
- Biometric security systems for hand recognition.
Problems Solved:
- Enhancing image quality through normal estimation.
- Providing accurate surface normal information for hand images.
- Enabling interactive applications for image augmentation.
Benefits:
- Improved image augmentation capabilities.
- Enhanced hand image analysis accuracy.
- Versatile applications in various industries.
Commercial Applications: The technology can be utilized in industries such as:
- Gaming and entertainment.
- Healthcare and medical imaging.
- Security and biometrics.
Questions about Hand Surface Normal Estimation: 1. How does the normal estimation model improve image augmentation? 2. What are the potential limitations of using hand surface normal estimation in real-time applications?
Frequently Updated Research: Stay updated on advancements in hand surface normal estimation technology to leverage the latest developments in image augmentation and analysis.
Original Abstract Submitted
an system for augmenting images using hand surface normal estimation is provided. in a model training phase, 3d models of hands are generated using 3d data of hands in a variety of positions. target normal training data is generated that includes normals of surfaces of the 3d models and synthetic 2d image training data corresponding to the 3d models and the normals. the target normal training data and the synthetic image training data are used to train a normal estimation model. the normal estimation is used by an interactive application to generate augmentations that are applied to hand image data.
