Portrait Stylization Framework Blending Latent Codes
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Solution Overview
Problem
Existing portrait stylization methods struggle to balance stylization and personalization, often resulting in images that either lack personalization or fail to incorporate stylized subjects effectively.
Innovation Solution
The proposed solution involves blending latent codes from a portrait stylization model with those from another StyleGAN-based method, allowing users to interactively control the amount of stylization and personalization applied to an input image. This is achieved through an AgileGAN framework that utilizes a hierarchical variational autoencoder for inversion-consistent transfer learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If more stylization is applied to an image, then the stylization quality is improved, but the personalization of the subject deteriorates
Solution Approach 1:
The latent code is segmented into multiple components (identity code, style code, and blended code) that can be independently controlled. This allows separate manipulation of personalization and stylization aspects, resolving the contradiction by enabling both high stylization quality and preserved personalization through coordinated use of different code segments
Solution Approach 2:
The system changes parameters by introducing a blending coefficient that controls the proportion of original vs. stylized latent codes. By adjusting this parameter, the system can achieve different levels of stylization while maintaining personalization, effectively resolving the trade-off between the two opposing requirements
2Loss of information
If more personalization is maintained in an image, then the personalization is improved, but the stylization quality deteriorates
Solution Approach 1:
The latent code is segmented into multiple components (identity code, style code, and blended code) that can be independently controlled. This allows separate manipulation of personalization and stylization aspects, resolving the contradiction by enabling both high stylization quality and preserved personalization through coordinated use of different code segments
Solution Approach 2:
The system creates a composite latent code by combining original latent code and stylized latent code in configurable proportions. This composite approach allows the final representation to contain both personalization information and stylization information, simultaneously achieving both goals that were previously mutually exclusive
3Measurement precision
If supervised approaches are used for portrait stylization, then the training accuracy is improved, but the data preparation complexity increases
Solution Approach 1:
Instead of using supervised approaches that require paired datasets (image-to-image translation), the system inverts the approach by using unpaired datasets and employing GAN-based unsupervised learning. This inversion eliminates the need for complex manual data pairing while maintaining training effectiveness through adversarial training mechanisms
Data Source
AI summary
Systems and methods directed to controlling the similarity between stylized portraits and an original photo are described. In examples, an input image is received and encoded using a variational autoencoder to generate a latent vector. The latent vector may be blended with latent vectors that best represent a face in the original user portrait image. The resulting blended latent vector may be provided to a generative adversarial network (GAN) generator to generate a controlled stylized image. In examples, one or more layers of the stylized GAN generator may be swapped with one or more layers of the original GAN generator. Accordingly, a user can interactively determine how much stylization vs. personalization should be included in a resulting stylized portrait.