Spatial Realism Predictor for Localized GAN Image Assessment
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Solution Overview
Problem
Existing image synthesis techniques, particularly generative adversarial networks (GANs), fail to explicitly encode or evaluate semantically important properties like spatial geometry and facial symmetry, leading to perceptually distorted and unnatural outputs, and lack localized assessment of image realism.
Innovation Solution
A deep convolutional neural network-based image realism predictor is trained using a dataset of computer-generated images with corresponding ground truth maps to produce a spatial realism heatmap, highlighting realistic and unrealistic regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative adversarial networks (GANs) are used for image synthesis, then image generation capability is improved, but spatial geometry and facial symmetry properties are not explicitly encoded or evaluated leading to perceptual distortion
Solution Approach 1:
The patent introduces an intermediary realism predictor network that acts as a mediator between the GAN generator and the final image output. This predictor network evaluates spatial realism properties (geometry, symmetry, proportions) and provides feedback to guide the GAN optimization, thereby enforcing precise spatial properties without directly modifying the image synthesis process
Solution Approach 2:
The patent implements a feedback mechanism where the realism predictor generates spatial realism maps that are backpropagated to the GAN generator during training. This feedback loop allows the system to learn and enforce spatial geometry and symmetry constraints iteratively, improving manufacturing precision while maintaining productivity
2Productivity
If statistical measures (Inception Score, Fréchet Inception Distance) are used to evaluate GAN performance, then evaluation speed is improved, but semantically important properties like spatial geometry are ignored
Solution Approach 1:
The patent segments the image evaluation process into multiple specialized components: the realism predictor network is divided into separate evaluation modules that independently assess different spatial properties (geometry, symmetry, proportions). This segmentation allows each module to focus on specific semantic properties while maintaining overall evaluation efficiency
Solution Approach 2:
The patent applies local quality by generating spatial realism maps that provide localized assessment of different image regions. Instead of a single global score, the system produces pixel-level or region-level realism predictions that highlight specific areas with geometric or symmetry issues, enabling precise measurement of spatial properties
3Reliability
If GAN discriminator is used to assess image realism, then assessment capability is improved, but localized assessment of image realism is not provided
Solution Approach 1:
The patent transitions from a single-value realism assessment to a multi-dimensional evaluation by producing spatial realism maps with spatial coordinates. The realism predictor outputs not just a realism score but also spatial location information, adding the dimension of spatial positioning to the assessment and preserving location information that would otherwise be lost
Data Source
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AI summary
A method for predicting the realism of an object within an image includes generating a training image set for a predetermined object type. The training image set comprises one or more training images at least partially generated using a computer. A pixel level training spatial realism map is generated for each training image of the one or more training images. Each training spatial realism map configured to represent a perceptual realism of the corresponding training image. A predictor is trained using the training image set and the corresponding training spatial realism maps. An image of the predetermined object is received. A spatial realism map of the received image is produced using the trained predictor.