# DeepProfile: Accurate Under-the-Clothes Body Profile Estimation
## 2 Related Works
### Image-Based Human Segmentation  
General image segmentation is typically used to locate objects and boundaries in images. [PERSON] et al. [6] introduced one of the first deep learning works for semantic image  
Figure 1: An example of profile in existing segmentation dataset. (**a**) Input image and (**b**) corresponding profile.  
segmentation using a fully convolutional network. Later, deep-learning based semantic segmentation models, such as encoder-decoder architectures [7], regional convolutional networks (R-CNN) [8], Dilated Convolutional Models (ASPP) [9], Gated shape CNNs [10], Attention-Based Models [11], few-shot models [12] and Pyramid Networks [13] have been proposed. General image segmentation methods can be easily applied for extracting human body contours with the corresponding datasets.  
One of the popular topics in image segmentation is human segmentation, which can be classified into two categories, human parsing and pose-based human segmentation. On the one hand, human parsing is to segment a human image into different fine grained semantic parts, such as head, torso, arms and legs. [PERSON] et al. [14] first proposed solving the problem of human parsing as a labeling problem, where images are segmented into superpixels. [PERSON] et al. [15] introduced a method to seamlessly integrate human parsing and pose estimation within a unified framework by utilizing Parselets and Mixture of joint-group templates. [PERSON] et al. [16] proposed a quasi-parametric human parsing model with a specially designed matching convolutional neural network (M-CNN). [PERSON] et al. [2] labeled the human parsing dataset LIP and proposed the JPPNet, which uses multi-scale features and iterative refinement for human parsing. To increase the accuracy of boundary area segmentation results, [PERSON] et al. [1] added an edge-perceiving module to integrate the characteristic of object contour to refine the boundaries of parsing. On the other hand, pose-based human segmentation approaches generate human instance segmentation with the help of pose estimation. Pose2 Instance [17] is a human pose-conditioned segmentation model that adopts a cascade network to improve instance-level person segmentation. However, it relies on human detection and the performance drops when bounding boxes have large overlaps. PersonLab [18] is an approach for the tasks of pose estimation and human instance segmentation using an efficient single-shot model. It treats human body segmentation as a pixel-wise clustering and employs human pose to refine the clustering results. Pose2 Seg [19] concatenates the human pose skeleton feature to the image feature in the network to improve human instance segmentation, especially in the case of occlusion. It detects human body key parts and then builds up a segmentation mask on top of the key parts. However, the human body segmentation results generated by the aforementioned methods include information of the clothes the person wears, which does not make for accurate human profiles.  
Our method is to estimate the under-the-clothes body profile even when people are dressed in loose-fitting clothing.