MODNet

2000년 도입 · 논문 4편에서 사용

MODNet is a light-weight matting objective decomposition network that can process portrait matting from a single input image in real time. The design of MODNet benefits from optimizing a series of correlated sub-objectives simultaneously via explicit constraints. To overcome the domain shift problem, MODNet introduces a self-supervised strategy based on subobjective consistency (SOC) and a one-frame delay trick to smooth the results when applying MODNet to portrait video sequence. Given an input image $I$, MODNet predicts human semantics $s\_{p}$, boundary details $d\_{p}$, and final alpha matte $\alpha\_{p}$ through three interdependent branches, $S, D$, and $F$, which are constrained by specific supervisions generated from the ground truth matte $\alpha\_{g}$. Since the decomposed sub-objectives are correlated and help strengthen each other, we can optimize MODNet end-to-end.

출처: MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition

소개 논문: MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition