Research on Weakly Supervised Face Segmentation Technology Based on Visual Large Models in New Media Post-Production

Face segmentation is a critical component in new media post-production, enabling the precise separation of facial regions from complex backgrounds at the pixel level. With the increasing demand for flexible and efficient segmentation solutions across diverse media scenarios—such as variety shows, period dramas, and other productions—there is a pressing need for adaptable methods that can perform reliably under varying conditions. However, existing approaches primarily depend on fully supervised learning, which requires extensive manual annotation and incurs high labor costs. To overcome these limitations, we propose a novel weakly supervised face segmentation framework that leverages large-scale vision models to automatically generate high-quality pseudo-labels. These pseudo-labels are then used to train segmentation networks in a dual-model architecture, where two complementary models collaboratively enhance segmentation performance. Our method significantly reduces the reliance on manual labeling while maintaining competitive accuracy. Extensive experiments demonstrate that our approach not only improves segmentation precision and efficiency but also streamlines post-production workflows, lowering human effort and accelerating project timelines. Furthermore, this framework reduced reliance on annotations in the field of weakly supervised learning for facial image processing in the new media post-production scenario.