GAN Face Aging with Patch-Based Spatial Supervision
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Solution Overview
Problem
Current face aging technologies produce low-quality, high-resolution images with limited aging options, failing to capture fine details and individual variations, and lack control over the aging process, making them unsuitable for real-world applications.
Innovation Solution
The use of ethnicity-specific aging information and weak spatial supervision in a GAN-based model, with patch-based training and aging maps to guide the aging process, allowing for continuous and fine-grained control over facial aging signs, enabling realistic high-resolution transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If GANs are used for face aging, then learning-based approach is achieved, but results lack quality and have limited aging options
Solution Approach 1:
The patent applies local quality by dividing the face into multiple patches and applying different aging transformations to different regions. Each patch can be aged independently with specific transformations (rotation, flipping, scaling) to create diverse aging effects while maintaining overall face coherence. This resolves the contradiction by enabling both automated learning and high-quality diverse aging results.
Solution Approach 2:
The patent segments the face image into multiple patches for processing. By dividing the face into regions and applying different aging transformations to each patch, the system achieves both automated learning through GANs and high-quality diverse aging results. The segmentation enables fine-grained control over aging transformations while maintaining computational efficiency.
2Manufacturing precision
If additional tweaks and modifications are made to StarGAN, then aging quality improves, but model complexity increases
Solution Approach 1:
The patent introduces dynamic patch transformations that can be applied during training and inference. By dynamically rotating, flipping, and scaling patches based on their location and characteristics, the model achieves high-quality aging results without requiring complex architectural modifications to the base GAN. This resolves the contradiction by maintaining model simplicity while achieving superior aging quality through dynamic processing.
3Ease of manufacture
If face aging is treated as step-wise process with age bins, then training is simplified, but continuous aging control is lost
Solution Approach 1:
The patent implements continuous aging control by applying transformations with varying intensities and combinations rather than discrete age bins. The system can apply different numbers of transformations, different rotation angles, and different scaling factors to achieve continuous aging effects. This resolves the contradiction by maintaining training simplicity while enabling fine-grained continuous aging control.
4Manufacturing precision
If high-resolution images are processed, then fine details are captured, but computational resource usage increases
Solution Approach 1:
The patent divides high-resolution face images into multiple smaller patches for processing. This segmentation reduces the computational burden on each processing unit while maintaining the ability to capture fine details across the entire high-resolution image. The patch-based approach enables parallel processing and reduces memory requirements, resolving the contradiction between high-resolution processing and computational efficiency.
Solution Approach 2:
The patent applies partial transformations to patches rather than processing entire high-resolution images with full transformations. By applying transformations selectively to patches and using data augmentation techniques, the system captures fine details with reduced computational resource usage compared to processing complete high-resolution images with exhaustive transformations.
Data Source
AI summary
There are provided computing devices and methods, etc. to controllably transform an image of a face, including a high resolution image, to simulate continuous aging. Ethnicity-specific aging information and weak spatial supervision are used to guide the aging process defined through training a model comprising a GANs based generator. Aging maps present the ethnicity-specific aging information as skin sign scores or apparent age values. The scores are located in the map in association with a respective location of the skin sign zone of the face associated with the skin sign. Patch-based training, particularly in association with location information to differentiate similar patches from different parts of the face, is used to train on high resolution images while minimize resource usage.