Facial Image Generation Vector Space Segmentation
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Solution Overview
Problem
Conventional facial image generation techniques fail to create new faces that propagate a non-identifying characteristic from an original face while changing the identity-related characteristic, limiting their applicability in generating diverse facial images for training machine learning models.
Innovation Solution
A facial image generation system that encodes non-identity-related and identity-related attributes using vector representations in specific vector spaces, generating a modified latent vector to produce images with the desired attributes, allowing for the creation of faces with retained non-identifying characteristics and altered identity-related features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional facial image generation techniques are used to generate a new face based on an original face, then the identity of the original face is propagated to the new face, but the non-identifying characteristic (e.g., hair color) cannot be changed
Solution Approach 1:
The patent segments the facial image into two independent attribute spaces: identity-related attributes and non-identity-related attributes. By encoding these attributes separately into distinct vector representations, the system can independently modify one attribute while preserving the other, resolving the contradiction between identity consistency and characteristic variability.
Solution Approach 2:
The patent introduces a latent vector space as an intermediary representation between the original facial image and the generated modified image. This latent space allows for independent manipulation of identity and non-identity attributes through vector arithmetic operations, enabling the system to change non-identifying characteristics while maintaining identity propagation reliability.
2Adaptability or versatility
If conventional facial image generation techniques are used, then a new face is generated based on the original face, but the identity-related characteristic cannot be changed while retaining non-identifying characteristics
Solution Approach 1:
The patent divides the facial attribute representation into separate identity and non-identity components in the latent space. This segmentation enables independent control over identity-related characteristics while maintaining non-identifying attributes, achieving the desired adaptability through a systematic approach that manages process complexity.
Solution Approach 2:
The patent utilizes parameter changes in the latent vector space to achieve attribute modification. By adjusting specific parameters (vectors) corresponding to different attribute dimensions, the system can change identity-related characteristics while preserving non-identifying ones, providing a controlled and manageable process.
3Adaptability or versatility
If facial images are generated for training machine learning models, then diverse facial images are needed, but conventional techniques cannot generate faces with both retained non-identifying attributes and altered identity-related attributes
Solution Approach 1:
The patent segments attribute information into separate identity and non-identity vectors, allowing the system to preserve non-identifying attribute information while introducing varied identity-related attributes. This segmentation prevents information loss by maintaining distinct representations of different attribute types throughout the generation process.
Solution Approach 2:
The patent employs feedback mechanisms during the generation process to ensure that non-identifying attributes are accurately preserved while identity-related attributes are appropriately modified. This feedback loop maintains attribute information integrity and enables the generation of diverse facial images suitable for machine learning training.
Data Source
AI summary
Systems and methods for facial image generation are described. One aspect of the systems and methods includes receiving an image depicting a face, wherein the face has an identity non-related attribute and a first identity-related attribute; encoding the image to obtain an identity non-related attribute vector in an identity non-related attribute vector space, wherein the identity non-related attribute vector represents the identity non-related attribute; selecting an identity-related vector from an identity-related vector space, wherein the identity-related vector represents a second identity-related attribute different from the first identity-related attribute; generating a modified latent vector in a latent vector space based on the identity non-related attribute vector and the identity-related vector; and generating a modified image based on the modified latent vector, wherein the modified image depicts a face that has the identity non-related attribute and the second identity-related attribute.