Pose-Aware Image Generation Using Assistant Feature Vectors
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Solution Overview
Problem
Existing facial recognition technologies face difficulties in generating natural facial images when the pose of the input image is not considered, leading to challenges in transferring feature vectors and generating face components effectively.
Innovation Solution
The method involves receiving an input image and rotation information, generating a feature vector based on pose information, creating an assistant feature vector to represent target components, and generating a target image with the desired pose by combining the feature vector and assistant feature vector using neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pose information is not considered in facial recognition, then the system is simpler to implement, but it becomes difficult to generate natural facial images when pose changes occur
Solution Approach 1:
The feature vector is segmented into multiple components including identity feature vectors and pose feature vectors. By separating pose information from identity information, the system can selectively process and transfer relevant features while maintaining natural image generation across different poses.
Solution Approach 2:
An assistant feature vector is introduced as an intermediary element that bridges the gap between the input feature vector and the target image generation. This assistant feature vector specifically represents target components according to the desired pose, enabling natural image synthesis while considering pose changes.
2Reliability
If pose information is considered and assistant feature vectors are generated, then natural facial image generation is improved, but the processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary processing by generating the assistant feature vector before the final image synthesis step. This assistant feature vector pre-encodes the pose-specific target components, so that during actual image generation, the system can directly combine feature vectors without complex real-time pose analysis.
Solution Approach 2:
The solution adds a new dimension to the feature representation by introducing the assistant feature vector that specifically encodes pose-related target components. This additional dimensional information allows the system to handle pose variations without fundamentally complicating the existing image generation architecture.
Data Source
AI summary
A processor-implemented image processing method and apparatus are provided. The image processing method includes receiving an input image and rotation information associated with the input image, generating a feature vector of the input image based on pose information corresponding to the input image, generating an assistant feature vector which represents a target component according to a pose corresponding to the rotation information, based on the feature vector, the pose information, and the rotation information, and generating a target image which has the pose corresponding to the rotation information based on the feature vector and the assistant feature vector.