Try-On Image Generation Using Pose-Aligned Clothing Deformation
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Solution Overview
Problem
Existing virtual try-on technologies fail to consistently preserve clothing details and accurately restore the original physical characteristics of the model, often resulting in deformed or unrealistic try-on images.
Innovation Solution
A method for generating try-on images that involves obtaining a target model and clothing images, performing image processing to extract control information, applying clothing deformation to align the clothing shape with the model's pose, and using a try-on image generation model based on Latent Diffusion Models (LDMs) to generate high-fidelity images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing virtual try-on methods are used, then the generation speed is improved, but the clothing details are not preserved and clothing deformation occurs
Solution Approach 1:
The patent segments the image processing into multiple distinct modules: pose estimation extracts body pose information, segmentation separates the model from background, and inpainting restores occluded regions. This modular segmentation allows each module to specialize in specific tasks, maintaining high generation speed while preserving clothing details through coordinated processing.
Solution Approach 2:
The patent performs preliminary actions by first estimating the model's pose and segmenting the model before generating the try-on image. This preliminary processing prepares the input data in advance, enabling the generation model to focus on detail preservation without compromising speed, as the structural information is already extracted and organized.
2Loss of time
If existing virtual try-on methods are used, then the processing time is reduced, but the model's physical characteristics are not accurately restored
Solution Approach 1:
The patent implements feedback mechanisms where the generated try-on image is compared with the original model image to identify discrepancies in physical characteristics. The system uses this feedback to iteratively adjust the generation process, ensuring accurate restoration of body shape and pose while maintaining efficient processing through optimized feedback loops.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on the specific characteristics of the input images. By changing parameters such as diffusion steps, sampling rates, and processing resolution adaptively, the system achieves accurate physical characteristic restoration without requiring excessive processing time for all cases.
3Device complexity
If existing virtual try-on methods are used, then the system complexity is reduced, but the try-on image realism is compromised
Solution Approach 1:
The patent introduces intermediary components such as pose estimation models and segmentation networks that act as mediators between the input images and the final try-on generation. These intermediaries process and transform the input data into structured representations, enabling realistic image generation without requiring the main generation model to handle all processing tasks, thus balancing complexity and realism.
Data Source
AI summary
A try-on image generation method includes: obtaining a first image of a target model and a second image of an item of clothing to be tried on; performing image processing on the first image to generate a plurality of third images, each expressing different information; performing clothing deformation processing on the item of clothing in the second image based on the first image to obtain a fourth image, wherein a clothing shape in the fourth image aligns with a pose of the target model; and generating a try-on image of the target model wearing the clothing in the corresponding pose based on the third images, the fourth image, the first image, and the second image.