Dense Image Correspondence Training Using 3D Geodesic Loss
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Solution Overview
Problem
Existing computer vision models struggle to accurately identify correspondences across images, particularly with subjects like humans, due to variations in body orientation, clothing, and confusing similarities, leading to incorrect associations and high costs of human-annotated training data.
Innovation Solution
Training models using synthetic data from 3D models of subjects, incorporating geodesic distances to generate loss values, enabling accurate differentiation of features in 2D images and reducing errors in predicting correspondences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-annotated training data is used to train models, then models can learn to identify correspondences in images, but the cost of creating training data becomes very high and measurement precision deteriorates due to human inability to identify features precisely
Solution Approach 1:
The patent uses 3D models as synthetic copies of real subjects to generate training data. Instead of relying on expensive human annotation of real images, the system creates realistic training examples by rendering 3D models in various poses and conditions, then uses these synthetic image pairs with known ground truth correspondences to train the model.
Solution Approach 2:
The system uses geodesic distances calculated from 3D models to automatically generate loss functions for training. The 3D model itself provides the ground truth correspondence information through geometric calculations, eliminating the need for external human annotators and enabling self-supervised learning.
2Reliability
If human annotators manually code features in images, then training data can be created, but incorrect associations are learned when features are misidentified (e.g., cheek pixels associated with shoulder)
Solution Approach 1:
By using 3D model copies, the system obtains perfect ground truth correspondence information through geometric calculations. The synthetic training data generated from 3D models eliminates the ambiguity and errors inherent in human annotation, providing reliable supervision signals for learning accurate feature associations.
Solution Approach 2:
The patent transforms the problem from 2D image feature matching to 3D geometric correspondence. By incorporating geodesic distances and spatial relationships from 3D models as additional training parameters, the system provides more discriminative information that helps distinguish between similar-looking but semantically different regions.
3Adaptability or versatility
If models are trained on limited human-annotated data, then training is feasible, but the model cannot generalize well to images with variations in body orientation, clothing, and camera angles
Solution Approach 1:
The system dynamically generates diverse training data by posing 3D models in various configurations - different body orientations, camera angles, and simulated clothing variations. This allows the model to learn robust correspondences across many conditions from a single 3D model, greatly enhancing generalization capability without requiring diverse real-world annotated datasets.
Solution Approach 2:
A single 3D model serves multiple functions: it generates training data for various poses, provides ground truth correspondences, and can be used to create data for different subjects. This universal approach allows the system to learn from limited real data while maintaining the ability to generalize across diverse scenarios.
Data Source
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AI summary
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.