## 1 Introduction  
The recent and rapid development in computer vision has facilitated fashion analysis in recommender systems. In the past, the task has been to find a substitute or complement for a given item based on visual signals extracted from an item's image. In addition to learning the visual distance between a pair of items, recent research has shifted focus to learning the compatibility between a set of fashion items and recommending garments to users. The images used in such tasks are of high quality, where items are centered on a white background photo, as shown in Figure 0(a). However, the majority of real-world fashion images usually contain complex scene information and human subjects in various poses, such as Figure 0(b), and the individual image of each item appearing in the outfit is not available. Shop The Look (STL) [1] was the first to bridge the gap between real-world scene-based images and product-based images, which can extract the same item from online shops for a given street photo with a query box. [PERSON] et al. [2] extended it and proposed a task called Complete The Look (CTL). CTL first removes the query item from the scene via image cropping. Then it keeps the larger one from the remaining 'top' and 'bottom' regions of the fashion scene, and uses the visual distance to find the item that can best match the cropped scene. However, the category information of the removed item (e.g., 'tops') should be given to the CTL model, and the visual distance between the cropped scene and the item is only computed at a global image level and local region level, where item objects and human subjects appearing in the scene are ignored. Studies [3,4] have shown that personalization is an important topic for improving recommendation accuracy. It is easy to achieve personalized recommendation on online shopping platforms since user profiles are available. For real-world fashion images, user profiles are not available, but personalization can still be achieved by extracting human information (e.g., facial features, body shapes) from images. [PERSON] et al. [5] were the first to combine body-shape estimation with clothes recommendation, where body shape was obtained by reconstructing a human mesh from a single image. However, the recommended clothes types are limited to dresses and tops, since they only considered the compatibility between the body shape and the target item. They aimed at making a single body-specific item recommendation and could not be used for outfit completion. Another key topic in outfit completion is the prediction of the missing category from the given scene, since a correctly recommended outfit should be complete and contain no redundancy (e.g., an outfit should not contain two dresses). To deal with that, for product-based images, the model can limit item compositions and the processing order of an outfit (e.g., the items in an outfit should appear in the fixed order: tops, bottoms, shoes and accessories). For scene-based images, this demand can be satisfied only by providing the model with the missing category. To address the above challenges, in this paper, we design a Body shape-aware Object-level model to Complete The full-body Portrait image (BOCTP). The training data are obtained from the STL dataset with some pre-processing. With each STL data pair, there is a scene image with a bounding box and a ground-truth item image from the shop, which is the same item as the one in the bounding box. We remove the query item from the scene image by masking its corresponding bounding box in white. To train our holistic outfit-completion model, image-level, region-level and object-level distances, as well as body shape and category matching, are all taken into account. We start off with the premise that the visual contents of the scene and item are vital to compatibility learning. Image features extracted by ResNet are downscaled to obtain the scene and item embeddings, which can roughly represent the most important visual information of scenes and items.