Dual-Sample Avatar Reshaping Model Training for Attribute Retention
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Solution Overview
Problem
Existing image processing technologies struggle to accurately replace avatars in images while preserving the original attributes, leading to a lack of realism and limited application scenarios.
Innovation Solution
An image processing model is trained using a generative adversarial network (GAN) to replace avatars by learning the differences between target and sample avatars, while retaining the original attributes, through a process involving encoder-decoder structures and loss function optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing image processing technologies are used to replace avatars, then the replacement process can be completed, but the realism of the replaced avatar is low and original attributes are not well preserved
Solution Approach 1:
The patent segments the avatar replacement task into two distinct training processes: one focused on avatar replacement accuracy (using target avatars from the first sample set) and another focused on attribute preservation (using source avatars from the second sample set). This segmentation allows the model to separately optimize for realism and attribute retention, resolving the contradiction between these two requirements.
Solution Approach 2:
The patent changes the training parameters by introducing two different loss functions with different weighting factors. The first loss function emphasizes avatar replacement accuracy while the second emphasizes attribute preservation. By adjusting these parameters during training, the model achieves both high realism and accurate attribute retention simultaneously.
2Adaptability or versatility
If existing image processing technologies are used to replace avatars, then the processing can be completed, but the application scenarios are limited
Solution Approach 1:
The patent creates a universal image processing model that can handle multiple application scenarios through dual-sample-set training. The model is designed to work with different types of input images and preserve various attributes (pose, expression, lighting) simultaneously, making it adaptable to diverse applications such as virtual try-on, avatar animation, and image editing while maintaining high processing accuracy.
3Manufacturing precision
If single sample set training is used, then the training process is simple, but the model cannot simultaneously achieve avatar replacement and attribute retention
Solution Approach 1:
The training process is segmented into two distinct phases using two different sample sets. The first sample set trains the model for avatar replacement, while the second sample set trains it for attribute preservation. This segmentation increases precision but requires managing two training processes, which the patent handles through automated dual-sample-set loading and separate loss function calculations.
Solution Approach 2:
The patent merges the results of two separate training processes into a single unified model. By combining the learned features from both sample sets during the training phase, the model achieves both avatar replacement accuracy and attribute retention capabilities in one integrated system, offsetting the increased training complexity with streamlined inference.
Data Source
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AI summary
An image processing method, apparatus and device, and a computer-readable storage medium. The method comprises: invoking a first generator to separately perform generative processing on a first sample image and a second sample image to obtain a first predicted image and a second predicted image; and training an image processing model on the basis of the head portrait difference between the first sample image and the first predicted image, and the specified attribute difference between the second sample image and the second predicted image, the image processing model being used for replacing a head portrait in an input image with a target head portrait, and retaining a first type of attributes of the head portrait in the input image. Therefore, the authenticity of the replaced head portrait is high, and the accuracy of image processing and the replacement effect are improved.