## 1 Introduction  
In recent years, the rapid evolution of artificial intelligence (AI) and computer vision technologies has revolutionized China's media industry, particularly in the post-production of new media [1]. New media platforms, including mobile and PC-based platforms, now dominate content dissemination through video and graphic formats [2]. Post-production remains the cornerstone of the creation of new media content, enabling the production of visually sophisticated works that were previously unattainable, thus transforming the entire media landscape [3].  
New media post-production involves editing, refining, and processing raw materials (e.g., films, television shows, variety programs, and news) into polished final products [4, 5]. This process enhances content quality, diversifies visual storytelling, and meets evolving public demands [3]. Among its core tasks, face processing [6, 7], including special effects, face replacement, and modification, has become a critical focus. However, traditional methods such as rough cropping, image masking, or frame-by-frame deletion [8] remain inefficient and labor-intensive, especially in complex scenarios such as variety shows and period dramas.  
For example, in variety shows, dynamic camera movements, rapid scene transitions, and unpredictable actor positions often lead to partial face occlusion or motion blur, making pixel-level segmentation difficult. In period dramas, intricate costumes, dense background clutter, and historical clothing with textures similar to facial features further complicate the precision of segmentation [8]. These challenges are exacerbated by the reliance of existing face segmentation methods on fully supervised learning, which demands extensive manual pixel-level annotations. Such annotations are prohibitively time-consuming and costly, particularly for large-scale media productions where high-throughput post-processing is essential [9; 10].  
The rise of AI-driven deep learning has placed face segmentation as a key technology in new media post-production. Its accuracy directly impacts downstream tasks such as facial analysis, expression recognition, and AI-based face manipulation [11; 12]. However, the scalability of these models is constrained by their dependence on labor-intensive data labeling. To address these limitations, weakly supervised learning has emerged as a promising alternative, reducing the reliance on manual annotations while maintaining performance [13].  
This study presents a weakly supervised face segmentation framework specifically designed to address the complex demands of new media post-production through two key innovations:  
* A pseudo-label generation algorithm based on SAM is proposed to automatically generate pseudo-labels with near-expert quality, eliminating manual annotation efforts while maintaining high accuracy. Using the Segment Anything Model (SAM) [14] to automatically generate high-quality pseudo-labels, our approach enables the training of twin UNet++ networks that are capable of adapting to dynamic shooting conditions, such as rapid camera movements in variety shows, and visually challenging contexts, including the accurate separation of faces from elaborate period costumes.
* Dual-model fine-tuning using EMA is proposed, where both the student and teacher models share identical UNet++ structures but differ in parameter update mechanisms. This design avoids the error amplification issues in bidirectional frameworks and pure pseudo-label learning methods where noisy pseudo-labels from both models degrade performance. By decoupling pseudo-label generation (via stabilized teacher) and model learning (via active student), our method achieves robust segmentation in complex scenarios (e.g., separating faces from elaborate period costumes).  
Extensive experiments on self-collected datasets that include movies, period dramas, and variety shows confirm the effectiveness and generalizability of our approach with statistical significance.