Remove bg for Object

Image
Image
Examples

Remove bg for Object Introduction

Universal matting processes the main body in the input image, supporting goods, people, animals, plants, cars, etc., without any additional input, to achieve end-to-end universal matting and output four-channel matting results, as shown in the following figure:
Portrait Cutout Rendering

Model description

The model is divided into two subnetworks: rough segmentation and fine matting. The complex problem of matting is disassembled, first rough segmentation and then fine segmentation. Both networks are unet structures. The rough segmentation network predicts the rough semantic segmentation mask from the input image, and the fine segmentation network predicts the fine matting result based on the original image and the rough semantic segmentation mask.

How and where the model is expected to be used

How to use:

  • Direct inference, input image direct inference

    • Scope of use:

  • It is suitable for image segmentation with subject, and it is expected that the proportion of subject in the image is not too small
  • The desired effect can be achieved on images with resolution less than 2000×2000

    • Target scenario:

  • Scenes that require image matting, such as changing the background

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    Features

    Background removal is an image processing technique designed to separate the subject from the background in an image. Through the use of advanced computer vision algorithms, this process accurately and automatically removes bg from a photo, allowing the subject to stand out more prominently. This technology is commonly applied in image editing, advertising design, e-commerce, and social media sharing, providing users with flexibility and creative space to showcase image subjects in various scenarios.