Harmonizing composite images utilizing a semantic-guided transformer neural network

A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• receiving a composite image and a segmentation mask of a foreground object portrayed against a background of the composite image;
• utilizing a local neural network branch to extract local information from the composite image;
• utilizing a global neural network branch to extract global information of the background;
• utilizing a semantic neural network branch to extract semantic information from the composite image;
• and
• generating a harmonized composite image comprising the foreground object harmonized with the background by decoding the local information, global information, and the semantic information.

A system comprising:

• a composite image comprising a foreground object portrayed against a background of the composite image;
• a segmentation mask of the foreground object;
• a local neural network branch to extract local information of a background portion adjacent to the foreground object;
• a global neural network branch to extract global information of the background;
• a semantic neural network branch to extract semantic information from the composite image;
• a decoder to decode the local information, the global information, and the semantic information;
• and
• one or more processors configured to cause the system to generate a harmonized composite image comprising the foreground object with modified pixel color values based on decoding of the local information, the global information, and the semantic information.