Generative AI Virtual Try-On Latent Space Encoding
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Solution Overview
Problem
Existing virtual try-on solutions lack realism in presenting clothing items, failing to accurately depict how clothing would fit on a person based on a photograph of clothing.
Innovation Solution
A method and system using generative AI to provide virtual try-on by encoding input images and text descriptions into latent spaces, applying noise and iterative denoising processes within neural networks to generate realistic output images of a person wearing different clothing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional virtual try-on methods are used, then the process is simple and fast, but the realism and accuracy of clothing presentation is poor
Solution Approach 1:
The system segments the input image into multiple components: body region, original clothing region, and background. It also separates the clothing transfer process into encoding (extracting clothing features), denoising (refining the transferred clothing), and decoding (reconstructing the final image). This segmentation enables precise control over different regions and processes, improving clothing presentation realism while managing system complexity through modular processing.
Solution Approach 2:
The patent introduces intermediate representations including latent space encodings of the input image, clothing embeddings from the target clothing image, and pose embeddings that capture body posture. These intermediaries serve as mediators between the input data and final output, enabling the generative model to accurately transfer clothing while preserving body pose and background, thus improving realism without requiring direct pixel-manipulation complexity.
2Manufacturing precision
If generative AI with iterative denoising is used, then the realism of clothing visualization is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary encoding of the input image into latent space representation and extracts pose embeddings before the iterative denoising process. The target clothing is also pre-encoded to obtain clothing embeddings. These preliminary actions prepare all necessary features in advance, allowing the iterative denoising to focus solely on refining the clothing transfer, thus improving accuracy while minimizing additional processing time.
Solution Approach 2:
The patent utilizes parameter changes in the diffusion process by progressively adjusting the noise level across multiple denoising steps. It also employs conditioning parameters such as pose embeddings and clothing embeddings that guide the denoising process. By changing parameters strategically during iterative refinement, the system achieves high accuracy in clothing fit visualization while controlling the number of iterations needed, thereby managing processing time.
3Reliability
If the system preserves pose and background context, then the overall quality of virtual try-on is improved, but the complexity of image processing increases
Solution Approach 1:
The system applies local quality control by treating different regions of the image differently: the body region preserves original pose information, the clothing region undergoes generative transfer with target clothing features, and the background remains unchanged. Pose embeddings specifically guide the clothing region to conform to the body pose, while mask-based processing ensures only the clothing area is modified. This local differentiation improves overall output quality without requiring complex global processing of the entire image.
Solution Approach 2:
The generative model serves multiple functions simultaneously: it transfers target clothing to the body, preserves the original body pose through pose conditioning, maintains background consistency through mask-based processing, and ensures realistic lighting and texture through the diffusion process. This multi-functionality within a single integrated model improves reliability of the virtual try-on output while avoiding the need for separate specialized processing systems for each function.
Data Source
AI summary
Systems and methods for providing virtual try-on based on predetermined cloth patterns driven by generative AI are disclosed. An example method includes receiving an input image including an image of a body of a person and a first clothing worn by the person, receiving a text description of the first clothing and a further input image including a second clothing, encoding the input image into an image latent, generating a mask corresponding to the second clothing, encoding the text description into a text embedding, encoding the further input image into a clothing embedding, adding a noise to the image latent to obtain a noisy image latent, providing the noisy image latent, the text embedding, the clothing embedding, and the mask to a neural network to obtain a denoised image latent and decoding the denoised image latent into an output image including the body dressed in the second clothing.