DepthNet

Imagine a scenario where you want to modify the background while keeping the subject of the image consistent. How would you go about solving this problem?

One way to do this is by leveraging the Segment Anything Model (SAM), which we learned about in Chapter 16, and replacing the background with the background of your choice. However, there are two major problems associated with this method:

• The background is not generated, and so you will have to manually provide the background image.
• The subject and background will not be color-consistent with each other because we have done patchwork.

DepthNet solves this problem by leveraging a diffusion approach, where we will use the model to understand which parts of an image are the background and foreground using a depth map.

Workflow

DepthNet works as follows:

1. We calculate the depth mask of an image (depth is calculated by leveraging a pipeline similar to the one mentioned in the...