Developers replacing backgrounds with AI often struggle with unnatural color contamination, like yellow halos around hair edges. The problem stems from standard RGB blending, which mixes colors without considering human perception. This leads to composites that look obviously fake, even with perfect segmentation masks.

A multi-tier approach tackles this by combining robust segmentation with color space correction. The system uses BiRefNet for high-quality masks, U²-Net as a fallback, and traditional gradients as a final safety net. Moving the blending process to Lab color space allows precise removal of background color casts without affecting image lightness.

This architecture was built for VividFlow, an image-to-video tool with background swapping. The pipeline also includes semantic analysis to enhance user prompts and a dedicated cartoon art logic. The goal is reliable, production-grade results across diverse real-world images, moving beyond black-box tools to give developers more control.