Attribute Conditioned Image Generation Using Latent Space Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image editing techniques struggle to modify complex attributes such as facial expressions, age, and gender in images while preserving the identity and other attributes, often resulting in unintended changes due to the complex, interdependent encoding of features in neural networks.
Innovation Solution
The system generates a modified feature vector using a non-linear mapping function based on a latent vector representing the image, target attributes, and preserved attributes, allowing for precise alteration of specific attributes while maintaining other attributes unchanged, employing a neural network architecture that includes a mapping network and a generator network for image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image editing techniques are used to modify complex attributes, then the editing capability is limited, but the skill requirement increases significantly
Solution Approach 1:
The patent introduces an intermediary system consisting of a neural network with latent space representation and attribute conditioning mechanisms. This intermediary translates high-level attribute descriptions into modified feature vectors, enabling users to edit complex attributes like facial expressions and age without requiring manual editing skills. The system acts as a mediator between user intent and image modification, automatically handling the complex transformations.
2Ease of operation
If conventional image editing methods are used to change attributes, then editing simplicity is maintained, but unintended changes to other attributes occur
Solution Approach 1:
The patent applies local quality by conditioning the generation process on specific target attributes while preserving other attributes. The attribute conditioning mechanism allows the system to focus modifications locally on the desired attribute (e.g., facial expression) while maintaining the quality and integrity of other attributes (e.g., age, gender, identity). This selective modification approach ensures precision without requiring complex user intervention.
Solution Approach 2:
The system incorporates feedback through attribute conditioning, where the desired attribute values are fed back into the generation process to guide the modification. The conditional generation mechanism continuously references the target attributes during feature vector transformation, ensuring that the final image matches the intended attribute changes while preserving other characteristics. This feedback loop prevents unintended changes by constantly aligning the output with the specified attributes.
3Measurement precision
If manual image editing techniques are used, then attribute control is imprecise, but the process requires significant user skill
Solution Approach 1:
The patent extracts the complex attribute control logic from the user and encapsulates it within the neural network's latent space representation. By separating the attribute representation from the image generation process, the system achieves precise attribute control through mathematical operations in the latent space rather than manual pixel-level editing. This extraction of complexity into a dedicated representation layer enables precise control while keeping the user interface simple.
4Ease of operation
If traditional image editing software is used, then the interface is simple, but complex attribute changes require significant expertise
Solution Approach 1:
The patent replaces the mechanical system of manual image editing with an automated neural network-based system. Instead of requiring users to manually adjust pixels or use complex tools, the system substitutes mechanical editing operations with automated attribute conditioning and latent space transformation. This substitution enables complex attribute editing capabilities while maintaining interface simplicity, as users can specify desired attributes through high-level parameters rather than manual manipulation.
Data Source
AI summary
A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.