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Recently, many methods have united low-level and high-level features to generate the desired accurate high-resolution prediction for human parsing. Nevertheless, there exists a semantic-spatial gap between low-level and high-level features in some methods, i.e., high-level features represent more semantics and less spatial details, while low-level ones have less semantics and more spatial details. In this paper, we propose a Semantic-Spatial Fusion Network (SSFNet) for human parsing to shrink the gap, which generates the accurate high-resolution prediction by aggregating multi-resolution features. SSFNet includes two models, a semantic modulation model and a resolution-aware model. The semantic modulation model guides spatial details with semantics and then effectively facilitates the feature fusion, narrowing the gap. The resolution-aware model sufficiently boosts the feature fusion and obtains multi-receptive-fields, which generates reliable and fine-grained high-resolution features for each branch, in bottom-up and top-down processes. Extensive experiments on three public datasets, PASCAL-Person-Part, LIP and PPSS, show that SSFNet achieves significant improvements over state-of-the-art methods. [ABSTRACT FROM AUTHOR]