Modeling fashion compatibility between different categories of items and forming personalized outfits have become important topics in recommender systems recently. However, item compatibility and outfit recommendation have been explored in perfect settings in the past, where high-quality images of items from the front view or user profiles are available. In this paper, we propose a new task called Complete The full-body Portrait (CTP) for real-world fashion images (e.g., street photos and selfies), which is able to recommend the most compatible item for a masked scene where the outfit is incomplete. Visual compatibility and personalization are the key points for accurate scene-based recommendations. In our approach, the former is accomplished by calculating the visual distance of the query scene and target item in latent space, while the latter is achieved by taking the body-shape information of the human subject into consideration. To obtain side information to train our model, ResNet-50, YOLOv3 and SMPLify-X models are adopted to extract visual features, detect item objects, and reconstruct a 3D body mesh, respectively. Our approach first predicts the missing item category from the masked scene, and then finds the most compatible items from the predicted category through computing visual distances at image level, region level and object level, together with measuring human body-shape compatibility. We conduct extensive experiments on two real-world datasets, Street2Shop and STL-Fashion. Both quantitative and qualitative results show that our model outperforms all baseline models.