Fast Portrait Segmentation (FPS-Net)

Fast Portrait Segmentation (FPS-Net) represents a state-of-the-art leap in lightweight convolutional neural networks designed specifically for extracting human subjects from complex backgrounds in real-time. By 2026, the architecture has evolved from its initial U-Net origins into a hybrid attention-based MobileNetV4 backbone, optimized for edge-side inference on mobile NPUs and browser-based WebGPU environments. Unlike heavy vision transformers (ViT), FPS-Net prioritizes temporal consistency and boundary refinement, making it the industry standard for video conferencing and mobile AR applications. The technical architecture utilizes a multi-scale feature fusion module that captures both global semantic context (identifying the person) and local spatial details (preserving hair strands and clothing edges). In the 2026 market, it stands as the primary open-source alternative to proprietary solutions like Remove.bg or Canva’s Magic Studio, offering developers the ability to run 4K-resolution segmentation at 60FPS on consumer-grade hardware without the latency or privacy concerns of cloud-based APIs. Its deployment flexibility via ONNX, TFLite, and CoreML ensures seamless integration across iOS, Android, and Desktop platforms.