For human matting without the green screen, existing works either require auxiliary inputs that are costly to obtain or use multiple models that are computationally expensive. Consequently, they are unavailable in real-time applications. In contrast, we present a light-weight matting objective decomposition network (MODNet), which can process human 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. Moreover, since trimap-free methods usually suffer from the domain shift problem in practice, we introduce (1) a self-supervised strategy based on sub-objectives consistency to adapt MODNet to real-world data and (2) a one-frame delay trick to smooth the results when applying MODNet to video human matting. MODNet is easy to be trained in an end-to-end style. It is much faster than contemporaneous matting methods and runs at 63 frames per second. On a carefully designed human matting benchmark newly proposed in this work, MODNet greatly outperforms prior trimap-free methods. More importantly, our method achieves remarkable results in daily photos and videos. Now, do you really need a green screen for real-time human matting?

用一张单独的图像实现实时人像抠像的轻量级抠像分解网络（MODNet），其优化了一系列子目标同时通过显式约束、融合多尺度特征和采用自监督一致性策略来提高模型的效率和鲁棒性，且在 Adobe Matting 数据集和 PPM-100 基准测试中优于先前的自由 trimap 方法。

MODNet: 基于目标分解的实时无预分割抠图人像合成