Interactive Point-Based Image Editing via Diffusion Feature Maps
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Solution Overview
Problem
Existing image editing methods using deep generative models lack controllability and reliability, particularly in achieving precise pixel-level spatial control during interactive point-based image editing.
Innovation Solution
The proposed solution employs a diffusion model for interactive point-based image editing, where user input indicates handle and target points in a source image. A feature map is extracted using an inverse denoising diffusion process, updated based on user input, and then used to generate a target image through a denoising diffusion process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep generative models are used for image-to-image translation, then the ability to map images between domains is improved, but controllability and reliability deteriorate
Solution Approach 1:
The patent performs preliminary actions by extracting feature maps from the source image before editing, and by pre-defining handle points and target points that guide the editing process. The feature map extraction and point annotation are preparatory steps that enable controlled manipulation of specific image regions while preserving overall image coherence through the diffusion model's inherent structure.
Solution Approach 2:
The patent applies local quality by enabling selective editing of specific regions through handle-point and target-point annotations. Users can specify localized areas for modification while leaving other regions unchanged, allowing precise spatial control over which parts of the image undergo transformation. This localized approach maintains reliability by avoiding unnecessary changes to the entire image.
2Ease of operation
If interactive point-based editing is implemented, then user control over image modification is improved, but manufacturing precision deteriorates due to lack of pixel-level spatial control
Solution Approach 1:
The patent introduces an intermediary mechanism by utilizing feature maps as intermediate representations between user input and final image output. The diffusion model processes these feature maps through denoising steps, acting as a mediator that translates coarse user annotations into precise pixel-level modifications. This intermediary approach bridges the gap between simple user interaction and high-precision editing results.
Solution Approach 2:
The patent applies dimensionality change by operating in the feature map space rather than directly in pixel space. By extracting and manipulating feature representations (which add semantic and structural dimensions), the system achieves precise spatial control indirectly. The diffusion model's multi-step denoising process introduces temporal dimensionality, allowing gradual refinement from coarse to fine details.
3Measurement precision
If feature map extraction and updating is performed, then spatial control precision is improved, but device complexity increases
Solution Approach 1:
The patent applies the extraction principle by separating feature map processing from direct pixel manipulation. The diffusion model extracts meaningful features from the source image, and editing operations are performed on these extracted features rather than on raw pixels. This extraction approach simplifies the control mechanism while maintaining high precision, as features capture essential semantic and geometric information.
Solution Approach 2:
The patent utilizes parameter changes by modifying the diffusion model's internal parameters (feature maps) rather than directly changing image pixels. The handle-point and target-point annotations serve as control parameters that guide the transformation. By operating in the parameter space of the diffusion model, the system achieves precise spatial control through parameter optimization rather than complex structural modifications.
Data Source
AI summary
Embodiments of the disclosure relate to interactive point-based image editing. According to example embodiments of the present disclosure, a user edit input for a source image is obtained to indicate at least one handle point and at least one target point in the source image. A feature map is extracted from the source image using a diffusion model at an iteration step of an inverse denoising diffusion process performed on the source image. The feature map is then updated based on the user edit input. Then a target image is generated based on the updated feature map using the diffusion model through a denoising diffusion process performed on the updated feature map.