Dynamic Human Facial Image Synthesis via Parameterized Feature Segmentation
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Solution Overview
Problem
Current imaging processing methods can only generate static human facial images, limiting them to a single expression, whereas dynamic images capable of displaying multiple expressions are not effectively produced.
Innovation Solution
An image processing method and apparatus that obtain a human facial image and synthesize it into multiple frames of images based on preconfigured adjusting parameters, combining these frames into a dynamic image in a time order to create a sequence showing various expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static image processing methods are used to process human facial images, then the processing complexity is low, but the visual effect is limited to a single expression
Solution Approach 1:
The patent segments the human facial image into multiple independent regions (eyes, eyebrows, nose, mouth, cheeks) and applies different adjusting parameters to each region. This segmentation enables the generation of multiple expressions from a single facial image by independently manipulating each facial feature, thereby increasing expression variety without requiring multiple original images.
Solution Approach 2:
The patent transforms static facial images into dynamic expressions by applying adjusting parameters that modify facial features in different states. The system generates multiple frames with varying expressions (e.g., happy, sad, surprised) by dynamically adjusting parameters such as eye shape, mouth curvature, and cheek position, creating a sequence that resembles video or animation.
2Adaptability or versatility
If multiple source images are synthesized to create dynamic expressions, then the expression variety increases, but the processing time increases
Solution Approach 1:
The patent performs preliminary segmentation of the human facial image into key feature regions before expression generation. By pre-identifying and isolating facial features (eyes, eyebrows, mouth, etc.), the system prepares the image structure in advance, allowing for rapid application of adjusting parameters during expression synthesis without requiring extensive reprocessing for each expression frame.
Solution Approach 2:
The patent employs parameter-based adjustment of facial features instead of requiring multiple source images. By changing parameters such as eye opening size, mouth curvature, eyebrow position, and cheek elevation, the system generates diverse expressions from a single facial image, significantly reducing processing time compared to synthesizing multiple complete images.
3Adaptability or versatility
If facial features are adjusted to create different expressions, then the expression diversity improves, but the manufacturing precision requirements increase
Solution Approach 1:
The patent applies different adjusting parameters to specific local regions of the facial image rather than uniformly adjusting the entire image. Each facial feature (eyes, eyebrows, nose, mouth, cheeks) receives localized parameter adjustments tailored to its anatomical characteristics and expression requirements, enabling precise control over expression generation while maintaining natural facial proportions.
Solution Approach 2:
The patent uses dynamic parameter adjustment to create expressions, allowing flexible modification of facial features through controllable parameters. This approach enables precise expression generation by adjusting parameters such as eye shape, mouth curvature, and cheek position, with the ability to fine-tune each parameter independently to achieve desired expression precision.
Data Source
AI summary
An image processing method is provided. The method includes obtaining a human facial image and providing a total of n number of source images in a preconfigured file, where n is an integer greater than 2, and each source image corresponds to adjusting parameters for the source image in the preconfigured file. The method also includes generating a synthesized human facial image for the each source image by adjusting the human facial image based on the adjusting parameters corresponding to the source image in the preconfigured file, individually synthesizing the each source image and the synthesized human facial image for the each source image to obtain n number frames of synthesized images, and combining the n number frames of synthesized images into a dynamic image in a time order.