Systems and methods for using machine learning models to effect virtual try-on and styling on actual users

1. A method of virtual try-on and styling (VTO-S) of articles of clothing, the method comprising:

loading a user image, the user image including at least one of an image of a person performing the virtual try-on and styling (VTO-S), an image of a person related to the person performing the VTO-S, or any other image that the person performing the VTO-S wants to use for the virtual try-on and styling;

selecting a garment from a pre-existing database;

loading an image as a source of the selected garment, the image of the source of the selected garment forming a garment source image;

generating a semantic segmentation of the image;

extracting the selected garment from the image of the source based on the semantic segmentation;

determining a correspondence between a target from a user uploaded image or target option presented and a source by:

performing a feature point detection and description of both the target and the source using machine learning techniques, and

performing feature matching and correspondence validation;

performing garment warping and alignment of the extracted garment including deforming the extracted garment to match a shape and a pose of the target by utilizing a warping algorithm; and

overlaying the garment to generate an overlaid garment and rendering the overlaid garment on a user uploaded image with realistic texture mapping and shadow generation.

2. The method of claim 1 , further comprising fine tuning the garment fit to eliminate an imperfection in the overlaid garment using an iterative refinement technique and wherein the iterative refinement technique and a machine learning algorithm trained to identify and rectify common garment fitting imperfections are applied solely or in conjunction to eliminate imperfections.

3. The method of claim 2 , wherein the iterative refinement technique comprises calculating an error between the overlaid garment and the target, wherein the error calculation is based on factors including one or more of a garment's positioning, warping, and visual congruity with the target.

4. The method of claim 1 , further comprising performing an inpainting technique to eliminate visible artifacts or imperfections in the overlaid garment, wherein the inpainting technique utilizes both texture and image completion methodologies to maintain integrity of a visual appearance of the overlay garment.

5. The method of claim 1 , further comprising adjusting one or more of colors, shading, and textures of the overlaid garment to improve a match to lighting and appearance of the target, wherein the adjustment of colors, shading, and textures of the overlaid garment is performed using a combination of texture synthesis and color transfer techniques that account for the lighting and appearance and color of an image of the target.

6. The method of claim 1 , wherein the step of selecting a garment from a pre-existing database includes browsing and filtering garments based on category, style, color, or brand, wherein the browsing and filtering of garments in the pre-existing database is facilitated by an interactive user interface that provides options to sort and filter garments based on user preferences and previous interactions.

7. The method of claim 1 , wherein a user can upload the image of a target to wear the selected garment through a mobile or web application, and the method further includes a step to validate and process the uploaded image.

8. The method of claim 1 , wherein the step of generating a semantic segmentation of the image includes utilizing computer vision and machine learning algorithms to identify and separate regions of the image corresponding to the garment from a background, wherein the semantic segmentation of the image is performed using a machine learning algorithm trained on a large dataset of images with diverse garment types, body shapes, and poses.

9. The method of claim 1 , wherein the step of extracting the selected garment from the image of the source includes applying image processing techniques to isolate pixels corresponding to the garment based on the generated semantic segmentation, wherein the step of extracting the selected garment from the image of the source involves applying a mask generated from the semantic segmentation to the image.

10. The method of claim 1 , wherein the step of determining a correspondence between a target and the source includes utilizing feature point detection and description algorithms to identify distinctive points and descriptors on the target and the source, followed by feature matching and correspondence validation to establish corresponding points between the target and the source.

11. The method of claim 1 , wherein a correspondence model returns one or more corresponding key points such that more key points more accurately capture details of the garment and the shape of a body of the target, and wherein a warping model generates key points to capture deformations in the garment and a body shape of the target.

12. The method of claim 1 , wherein the step of overlaying and rendering the garment on the user uploaded image includes seamlessly blending the warped garment onto an image of the target, considering factors such as lighting, shadows, and reflections, and wherein the seamlessly blending of the warped garment onto the image of the target involves a blending technique to maintain consistency.

13. The method of claim 1 , further comprising performing automatic background removal and body segmentation on the garment source image before a garment warping, alignment, and rendering step.

14. The method of claim 1 , wherein the method further comprises adjusting a position, size, or orientation of the rendered garment on the user uploaded image, allowing a user to customize the virtual try-on experience, and wherein the user can manually adjust the position, size, or orientation of the rendered garment through a user interface that provides interactive controls and a real-time preview of the adjustments.

15. The method of claim 1 , wherein the method further comprises providing a comparison view that allows a user to switch between the rendered garment and a user's original image, facilitating a visual comparison of a user's appearance with and without the selected garment, and wherein the comparison view provides a side-by-side or overlay comparison, and the method further includes an option for the user to save or share the comparison view.

16. The method of claim 1 , further comprising applying a post-processing smoothing algorithm to the overlaying of the garment to improve visual integration of the garment with a body of the target.

17. The method of claim 1 , wherein the step of performing garment warping and alignment includes accounting for fabric behavior in fabric of the garment, characteristics of which are stored in the pre-existing database.

18. The method of claim 1 , wherein the user image is a full-body image, and the method further comprises automatically adjusting a size and position of the garment to maintain proportionality and correct placement on the target.

19. The method of claim 1 , further comprising providing a user interface that allows users to manually adjust a fit and position of the overlaid garment on the image of the target, the manually adjusting the fit and position of the overlaid garment including styling the clothing.

20. The method of claim 1 , wherein the step of overlaying and rendering the garment includes simulating an interaction of the garment with a target's body movements in a video uploaded by a user, providing a dynamic virtual try-on experience.