## 2 Related Works  
Traditional recommendation models (e.g., Collaborative Filtering (CF) [6]) aimed to learn the compatibility between users and items using Matrix Factorization (MF) [7]. Recently, [PERSON] et al. [8] improved CF by combining it with Deep Neural Networks to improve its limited expressiveness of high-rank relations. [PERSON] et al. [9] improved MF by assigning different confidence weights to unobserved user-item pairs. [PERSON] et al. [10] improved CF by using a graph neural network based on random walk.  
Later approaches sought to learn item relationships besides user-item compatibility. IBR [11] trained an embedding matrix to extract the most important features from item images and used the Mahalanobis distance to measure the compatibility between a pair of items. [PERSON] et al. [12] found that compatibility between items can exist in different ways (e.g., visually, functionally) and improved IBR by projecting the item into several spaces, and computing the weighted sum of visual distances in each space. [PERSON] et al. [13] used Generative Adversarial Networks [14] to generate a compatible item to help find complements. [PERSON] et al. [15] proposed self-attentive subset learning to give accurate recommendations. [PERSON] et al. [16] used tags as auxiliary information and used attention learning to capture high-level interaction features.  
Recent research has shifted attention to the topic of outfit recommendation for product-based images, which involves visual compatibility learning of more than two items. [PERSON] et al. [17] adopt a Bi-LSTM model to learn item compatibility in an entire outfit by modeling them as an ordered sequence, which ensures the outfit is complete and with no redundancy. [PERSON] et al. [4] propose GP-BPR to achieve personalized outfit recommendation, which uses the user's historical purchases. [PERSON] et al. [18] propose a graph-based network to make use of heterogeneous information.  
Despite the above improvements, product-based images are only a small part of fashion images, which mostly appear on specific online clothes stores. STL [1] first links product-based images with scene-based images, which can retrieve the same item from shops for the scene with a query box. The global distance between the scene and an item is computed by a visual similarity learning network. GRNet [19] refines it by building a similarity pyramid graph to obtain the local similarity for each region in the scene. CTL [2] extends STL and proposes to learn scene-item compatibility in scene-based images, where the compatibility is computed both globally and locally. However, the item objects and human subjects appearing in the scene are ignored.  
ViBE [5], trained on 958 dresses and 999 tops, is the first personalized outfit recommendation model for scene-based images. In contrast to product-based images, it is difficult to obtain user information such as user profiles and historical clicks from scene-based images.  
The development of human-mesh reconstruction from images such as SMPL [20] enables us to extract the subject's body shape from a single image. HMD [21] improves it by building a hierarchical reconstruction model through joint, anchor and vertex-level deformations. In addition, recently, SPIN [22] improves SMPL by fitting the model in the loop so that it is self-improving. [PERSON] et al. [23] propose SMPLify-X, which is able to build an expressive body model including 3D hands, face and body. To combine body information with outfit recommendation, ViBE first extracts the human body shape from the image using SMPL and HMD, and then defines the compatibility as the distance between the body shape and the target item embeddings. Similar to CTL, ViBE also ignores the compatibility between items in an outfit. Due to the constraints of its model, it can only recommend a single body-specific item.