Unified Human Image Editing for Pose and Part Replacement
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Solution Overview
Problem
Existing image generation models struggle to accurately perform both pose manipulation and part replacement tasks in human image editing, leading to inconsistencies and reduced fidelity in generated images.
Innovation Solution
A unified image generation model is trained on both pose modification and part replacement tasks, using a multi-task loss function and incorporating a part encoder, condition encoder, and pose-warping module to generate high-fidelity images that accurately reflect specified edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate diffusion models are used for different image editing tasks, then each model can be specialized for its specific task, but the overall system complexity increases and consistency across tasks deteriorates
Solution Approach 1:
The patent merges multiple task-specific diffusion models into a single unified image generation model that can handle pose manipulation, part replacement, and other image editing tasks. This consolidation reduces system complexity while maintaining the ability to perform diverse editing operations through a common architectural framework.
Solution Approach 2:
The unified image generation model is designed with multi-functionality to perform various image editing tasks including pose manipulation and part replacement. By making the model universal rather than task-specific, the system achieves reduced complexity while maintaining versatility across different editing scenarios.
2Device complexity
If a unified model is trained for multiple tasks, then system complexity is reduced, but the model's performance on specific tasks deteriorates
Solution Approach 1:
The training process is segmented into distinct phases where the unified model is first pre-trained on general image generation, then fine-tuned on specific tasks such as pose manipulation and part replacement. This segmentation allows the model to maintain strong foundational capabilities while achieving high performance on specific tasks through targeted fine-tuning.
Solution Approach 2:
The model employs parameter adaptation techniques where different portions of the model parameters are updated at different stages of training. Through carefully controlled parameter changes during fine-tuning, the unified model achieves optimized performance for specific tasks without losing the benefits of its universal architecture.
3Manufacturing precision
If diffusion models are used for image editing, then high-quality images can be generated, but the computational time and energy consumption increase
Solution Approach 1:
The model performs preliminary computations during the training phase to learn efficient representations and transformations. By pre-computing and storing useful feature mappings and attention patterns during training, the model can reduce computational energy during inference while maintaining high image quality through the learned efficient pathways.
Data Source
AI summary
A computer system and a computer-implement method include obtaining a source image and a modification input that indicates a target edit to the source image and generating a modification encoding representing the target edit. An image generation model generates an output image that depicts the source image with the target edit based on the source image and the modification encoding. The image generation model is trained to perform a pose modification task and a part replacement task.