Google llc (20240212325). Systems and Methods for Training Models to Predict Dense Correspondences in Images Using Geodesic Distances simplified abstract

Systems and Methods for Training Models to Predict Dense Correspondences in Images Using Geodesic Distances

Organization Name

google llc

Inventor(s)

Yinda Zhang of Daly City CA (US)

Feitong Tan of Beijing (CN)

Danhang Tang of West Hollywood CA (US)

Mingsong Dou of Cupertino CA (US)

Kaiwen Guo of Zurich (CH)

Sean Ryan Francesco Fanello of San Francisco CA (US)

Sofien Bouaziz of Los Gatos CA (US)

Cem Keskin of San Francisco CA (US)

Ruofei Du of San Francisco CA (US)

Rohit Kumar Pandey of Mountain View CA (US)

Deqing Sun of Cambridge MA (US)

Systems and Methods for Training Models to Predict Dense Correspondences in Images Using Geodesic Distances - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240212325 titled 'Systems and Methods for Training Models to Predict Dense Correspondences in Images Using Geodesic Distances

Simplified Explanation

The patent application describes systems and methods for training models to predict dense correspondences across images, such as human images, using synthetic training data created from 3D computer models of a subject. Geodesic distances derived from the surfaces of the 3D models are used to generate loss values for modifying the model's parameters during training.

• Models are trained to predict dense correspondences across images, particularly human images.
• Synthetic training data is created from 3D computer models of a subject.
• Geodesic distances from the surfaces of the 3D models are used to generate loss values.
• The loss values are used to modify the model's parameters during training.

Potential Applications

This technology can be applied in various fields such as computer vision, image processing, and artificial intelligence. It can be used in tasks like image alignment, object recognition, and pose estimation.

Problems Solved

This technology addresses the challenge of predicting dense correspondences across images accurately, especially in the context of human images. By using synthetic training data and geodesic distances, the model can be trained effectively to improve performance.

Benefits

The benefits of this technology include improved accuracy in predicting dense correspondences across images, enhanced performance in computer vision tasks, and the ability to train models effectively using synthetic data.

Commercial Applications

Title: Advanced Image Processing Technology for Enhanced Computer Vision Applications This technology can be commercialized in industries such as healthcare for medical image analysis, security for surveillance systems, and entertainment for augmented reality applications. It can also be used in robotics for object recognition and navigation.

Questions about the Technology

How does this technology improve the accuracy of predicting dense correspondences across images?

This technology utilizes synthetic training data and geodesic distances from 3D models to train models effectively, leading to improved accuracy in predicting dense correspondences.

What are the potential applications of this technology beyond computer vision tasks?

Apart from computer vision tasks, this technology can be applied in fields such as medical imaging, security systems, and entertainment for various applications requiring image analysis and processing.

Original Abstract Submitted

systems and methods for training models to predict dense correspondences across images such as human images. a model may be trained using synthetic training data created from one or more 3d computer models of a subject. in addition, one or more geodesic distances derived from the surfaces of one or more of the 3d models may be used to generate one or more loss values, which may in turn be used in modifying the model's parameters during training.