Generative Model Data Editing Apparatus for Flexible Image Region Modification
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Solution Overview
Problem
Class-conditional GANs lack flexibility in editing intermediate representations or conditional information, limiting the ability to modify specific image areas during image generation.
Innovation Solution
A data editing apparatus that uses generative models to receive change indications for specific data areas, generating new data by modifying intermediate representations and conditional information, allowing for flexible editing of partial image areas by iteratively transforming these variables within the generative model's architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If class-conditional GANs are used for image generation, then image generation capability is achieved, but flexible editing of partial image areas is not enabled
Solution Approach 1:
The patent segments the image into multiple partial areas (first data area and second data area) and applies different processing methods to each. The first data area undergoes iterative transformation through the generative model, while the second data area is generated directly from noise. This segmentation enables flexible editing of specific regions without affecting the entire image, resolving the contradiction between editing flexibility and system complexity.
Solution Approach 2:
The patent applies local quality by treating different parts of the image differently. The first data area receives iterative transformation with conditional information for flexible editing, while the second data area uses direct generation. This local differentiation enables precise control over specific image regions, achieving flexible editing capability without requiring the entire system to be overly complex.
2Adaptability or versatility
If iterative transformation of intermediate representation is applied, then flexible editing of partial areas is enabled, but processing time increases
Solution Approach 1:
By segmenting the image into areas requiring iterative transformation and areas that can be generated directly, the patent reduces overall processing time. Only the necessary first data area undergoes time-consuming iterative transformation, while the second data area is generated more efficiently from noise, thus balancing flexibility with processing time.
Solution Approach 2:
The patent applies partial action by performing iterative transformation only on the first data area that requires editing, rather than applying it to the entire image. This partial application of the time-consuming transformation process enables flexible editing where needed while avoiding unnecessary processing time elsewhere in the image.
3Adaptability or versatility
If generative models are used to generate data from noise, then data generation capability is achieved, but ability to edit specific features is limited
Solution Approach 1:
The patent applies preliminary action by generating the second data area directly from noise before the iterative transformation process. This preliminary generation of the non-editable portion allows the system to focus computational resources on the iterative transformation of the first data area, enabling feature editing capability while maintaining ease of overall data generation.
Solution Approach 2:
The patent segments the data generation process into two parts: direct generation from noise for the second data area, and iterative transformation for the first data area. This segmentation enables the system to maintain ease of data generation through direct noise sampling while achieving feature editing capability through selective iterative transformation of the first data area.
Data Source
AI summary
A flexible data editing scheme to change and modify an intermediate representation or conditional information for a portion of to-be-edited data is disclosed. One aspect of the present disclosure relates to a data editing apparatus, comprising: one or more memories; and one or more processors configured to receive a change indication to change at least a first data area of first data; generate second data by using one or more generative models and an intermediate representation for the first data area; and replace the first data area of the first data with the second data to generate third data.