Generative AI Ground Truth Synthesis via Pose-Preserving Masking
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Solution Overview
Problem
Generating ground truths for training generative neural networks is challenging due to the difficulty in obtaining input and output pairs, particularly when requiring specific poses and characteristics, such as a person wearing different dresses, which are hard to find in sufficient numbers.
Innovation Solution
A system is developed where a trained image generator neural network uses a small set of reference images to produce generated input and output pairs by maintaining pose and characteristics, with a mask generator determining which image areas to transform and blending preserved features back into the generated images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If ground truth data is obtained from existing image databases or manual collection, then training data availability improves, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system pre-trains a generative model on large-scale image databases to learn pose estimation and image generation capabilities in advance. This preliminary training enables the model to quickly generate ground truth data on-demand without requiring extensive manual data collection at deployment time, thus resolving the contradiction between data availability and time consumption.
Solution Approach 2:
The system uses the trained generative model to copy and transform reference images into synthetic ground truth pairs with different poses and characteristics. Instead of manually collecting diverse images, the system automatically generates multiple variations by copying and transforming a small set of reference images, thereby achieving high data availability with minimal time investment.
2Adaptability or versatility
If diverse poses and characteristics are required in training data, then model performance improves, but data collection complexity increases
Solution Approach 1:
The system employs a dynamic pose transformation mechanism that can generate diverse poses and characteristics on-demand using the trained generative model. Instead of statically collecting diverse images beforehand, the system dynamically generates the required diversity during training, reducing data collection complexity while maintaining model performance through adaptive pose and characteristic variations.
3Measurement precision
If manual annotation and verification are performed, then ground truth accuracy improves, but processing speed decreases
Solution Approach 1:
The system uses the trained generative model to automatically generate ground truth data with accurate pose annotations without requiring manual annotation. The model self-verify the generated images by comparing them against the reference images and training objectives, thereby achieving high accuracy while maintaining fast processing speed through automated generation and verification.
Data Source
AI summary
A first neural network is trained to generate a ground truth using a small set of example images that illustrate the goal ground truth output images, which can be full-body images of people in an AR style. The first neural network is used to generate ground truth output images from random input images. Example methods of the first neural network include determining poses in input images, changing values of pixels within areas of the input images, inputting the poses, the areas of the changed input images, and a text prompt describing the input images, into a neural network, to generate output images. The methods further include determining losses between the output images and the input images and updating weights of the neural network based on the losses. A second neural network is then trained using the generated ground truth. And, an application is generated that uses the second neural network.