GAN Image Shape Transformation via Segmentation Masks
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Solution Overview
Problem
Existing image translation technologies lack satisfactory performance in converting images between objects with different shapes, focusing mainly on style conversions between similar shapes.
Innovation Solution
A method using a generative adversarial network that includes generating segmentation masks, constructing a generator and discriminator, defining a loss function, and training the network to achieve image shape transformation between dissimilar objects, employing a cycle consistency and feature similarity loss function to ensure accurate shape transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generative adversarial network is used for image translation, then style conversion between similar shapes is achieved, but shape transformation between different objects is not satisfactory
Solution Approach 1:
The patent applies segmentation by dividing the image into foreground and background regions using a segmentation mask. This allows the GAN to independently transform the foreground object while preserving the background, enabling effective shape transformation between different objects by focusing the transformation process on segmented regions rather than treating the entire image uniformly.
2Reliability
If cycle consistency loss is used, then transformation consistency is improved, but training complexity increases
Solution Approach 1:
The patent implements feedback through cycle consistency loss, where the transformed image is passed through the inverse transformation and the result is compared with the original image. This feedback mechanism ensures that the forward and inverse transformations are consistent, improving reliability while the modular architecture keeps training complexity manageable.
3Manufacturing precision
If feature similarity loss is used, then shape transformation accuracy is enhanced, but computational cost increases
Solution Approach 1:
The patent applies local quality by computing feature similarity loss specifically on the foreground object regions identified by the segmentation mask, rather than computing it across the entire image. This focuses computational resources on the critical transformation areas, enhancing shape transformation accuracy while reducing overall computational cost by excluding background regions from the loss computation.
Data Source
AI summary
A generative adversarial network used for image shape transformation. The image shape transformation includes generating a segmentation mask of an image to be transformed and constructing a generator and a discriminator. The image shape transformation further includes constructing the generative adversarial network through the generator and the discriminator, constructing a loss function, training the generative adversarial network by a gradient descent method according to the loss function, and inputting the segmentation mask of the image to be transformed into the trained generative adversarial network to obtain an image shape transformation result.