Virtual Clothing Fitting via Segmentation and Dimensionality Change
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Solution Overview
Problem
Current image fusion models for virtual clothing changing often result in image generation distortion due to the loss of detail, leading to a poor effect in rendering the wearing effect of target clothing.
Innovation Solution
A method involving key point extraction, portrait segmentation, and human body part segmentation followed by inputting these features into a transformation model and a merging model to generate a high-fidelity image of a person wearing target clothing, ensuring accurate fitting and detail preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If an image fusion model extracts features from human body image and clothing image to generate a new image, then the virtual clothing changing function is achieved, but the loss of detail information occurs leading to image generation distortion
Solution Approach 1:
The patent segments the human body image into multiple components including key point features, portrait segmented image, and human body part segmented image. This segmentation allows detailed feature extraction from different regions, preserving information that would otherwise be lost in rough feature extraction, thereby improving image generation fidelity while enabling virtual clothing changing.
Solution Approach 2:
The patent transforms the 2D clothing image into a 3D garment model by introducing depth information and spatial relationships. This dimensional transformation allows the clothing to be properly fitted onto the 3D human body model, preventing distortion and maintaining high fidelity in the generated virtual try-on images.
2Productivity
If rough image features are extracted by the image fusion model, then the processing speed is improved, but the detail information is lost causing poor virtual clothing changing effect
Solution Approach 1:
The patent divides the image processing into multiple segmented stages: key point extraction, portrait segmentation, and human body part segmentation. Each segment processes specific features at appropriate detail levels, preserving important information while maintaining efficient processing through parallel and modular operations.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the human body image to extract key point features, portrait segmentation, and body part segmentation before the main clothing fusion process. This preliminary feature extraction prepares detailed information in advance, allowing the subsequent fusion process to maintain high fidelity without sacrificing processing efficiency.
3Ease of manufacture
If the clothing image is directly fused with the human body image, then the virtual clothing changing is achieved, but the clothing posture and size do not match the human body accurately
Solution Approach 1:
The patent performs preliminary transformation on the clothing image to adapt it to the human body's posture and dimensions before fusion. This includes adjusting clothing posture according to key point features and resizing according to body part measurements, ensuring accurate fitting is achieved before the final fusion step.
Solution Approach 2:
The patent changes key parameters of the clothing image including its posture angles, size dimensions, and spatial positioning to match the corresponding human body parameters. This parameter adaptation ensures the clothing accurately conforms to the body's shape and pose, improving fitting precision in the generated image.
Data Source
AI summary
Embodiments of the disclosure discloses a method, apparatus, device and storage medium for image generation. The method includes obtaining a first human body image comprising a target human body and a first clothing image comprising target clothing; performing key point extraction, portrait segmentation and human body part segmentation on the first human body image respectively, to obtain a key point feature image, portrait segmented image and human body part segmented image; inputting the key point feature image, the portrait segmented image, human body part segmented image and first clothing image into a transformation model, to obtain a transformed second clothing image; and inputting the second clothing image, first human body image, key point feature image, portrait segmented image and human body part segmented image into a merging model, to obtain a second human body image, wherein the target human body in the second human body image wears the target clothing.