Seismic data are generally sampled irregularly and sparsely along spatial coordinates because economic costs and obstacles hinder the regular arrangement of geophones in the field. Thus, the sampled seismic data often contain missing traces which result in difficulties for later processing steps. To alleviate this issue, versatile interpolation methods have been developed to interpolate the missing traces. However, the existing models for recovering seismic data with consecutively missing traces in a large amplitude range tend to produce artifacts and blurred signal details. We propose in this article a hybrid-loss-guided coarse-to-fine model which consists of a coarse network and a refinement network to allow different regions of seismic data to be recovered in different stages. The coarse network is designed to reconstruct strong signals, and the refinement network is implemented subsequently to recover weak signals. In addition, the refinement network focuses its attention on areas which are not well-recovered by the coarse network via a weight-masked mechanism. By resorting to the hybrid loss function L1 + structural similarity index measure (SSIM) + relativistic average least-square generative adversarial network (RaLSGAN), our model enables more accurate and realistic signal details to be reconstructed. Experiments with synthetic and field data demonstrate that our model is superior to the existing mainstream approaches, and the role of the key components is also investigated through ablation studies.