Virtual Try on - TryOnDiffusion: A Tale of Two UNets

• Transfer clothes between source, target
• Warping, blending
• Occlusion is challenging
• Diffusion models to handle issues

Warping - 

Warping involves transforming an image's geometry, usually to correct distortions, align images, or change the perspective. 

There are different types of warping, such as:

• Affine warping: This type of warping preserves parallel lines and involves a linear transformation followed by a translation. It can represent transformations like rotation, scaling, and shearing. 
• Perspective (projective) warping: This type of warping can represent a more general transformation that includes perspective changes. It can correct distortions caused by the camera's viewpoint or create a "bird's-eye view" of a scene. Perspective warping requires four pairs of corresponding points in the input and output images to calculate the transformation matrix.
• Warping is widely used in various applications, such as image stitching (for creating panoramas), rectifying images for OCR (Optical Character Recognition), and correcting lens distortions in photographs.

In the context of computer vision libraries like OpenCV, warping functions are available to apply these transformations to images, given the appropriate transformation matrix and input/output coordinates.

OpenCV Warping functions

• cv2.warpAffine
• cv2.warpPerspective
• cv2.remap

OpenCV Blending functions

• cv2.addWeighted
• cv2.add
• cv2.subtract

• Note #1 - All segmentation done on low resolution
• Note #2 - Super Resolution is added to cover up low res and give high res outputs
• Note #3 - Running all tasks on high res is even more challenging

Paper - link

Walmart Try on

Things to note

• Poster detection
• More minmal clothes and superimposition approach
• Full body posture + cloth overlap on it.

Keep Exploring!!!