# FA-VTON: A Feature Alignment-Based Model for Virtual Try-On
## 2 Related Works  
### Image-Based Virtual Try-On  
Image-based virtual try-on technology aims to seamlessly transfer selected garments onto target person images to produce realistic and natural-looking results. CAGAN [13] made the first attempt to address the limitations of training triplet data (original person image, chosen garment, and image post-try-on) using a cycle consistency structure, but the quality of the generated images fell short of expectations. VITON [5] broke down the virtual try-on process into three key stages: parsing the person image, warping the garment, and generating and refining the result. It introduced a person representation method that did not rely on the original garments but retained identity characteristics to compensate for the lack of supervised training data. However, even with these improvements, images produced by the VITON method still suffered from imperfections and bad quality. Presently, image-based virtual try-on methods follow the VITON framework, typically involving three main steps: parsing and extracting features from person and garment images, warping garments, and generating the final try-on results. To improve the quality of try-on images and eliminate imperfections, most approaches focus on refining the garment warping and try-on generation stages. For instance, CP-VTON [10] employs convolutional neural networks (CNNs) to learn Thin Plate Spline (TPS) [14] parameters for garment warping, resulting in better-fitting garments and more realistic try-on effects. However, CP-VTON struggles with complex garment deformations in different poses. ClothFlow [4] introduced appearance flow [15] to enhance garment adjustments. To tackle the challenge of complex poses, FS-VTON [7] adopts a StyleGAN [16]-based architecture to estimate a global appearance flow. RMGN-VTON [17] improves the generation network, which uses a regional mask to fuse the features of garment and person images. HR-VTON [9] proposed a try-on condition generator to warp garments and a try-on image generator guided by a segmentation map. Some methods utilize image inpainting and reconstruction techniques [18; 19; 20; 21; 22; 23] for virtual try-on tasks, such as TWD [24; 25; 26]. Despite these advancements in garment warping and try-on image generation, they ignore the impact of feature alignment. Since the garment warping module must refine features at different scales, the quality of extracted features directly influences the accuracy of garment warping. Poor quality features can lead to misaligned images, reducing the effectiveness of the warping module. Therefore, the design of a robust feature extraction module is critical in virtual try-on models. In this paper, we analyze existing shortcomings and explain the significance of the feature extraction module for garment warping and try-on image generation. Previous methods typically utilize two simple Feature Pyramid Networks (FPNs) [27] to extract multi-scale person and garment features, guiding garment warp based on the extracted features. FPNs are commonly used to address multi-scale information processing issues in tasks, such as object detection and semantic segmentation, improving the model's sensitivity to image details. Since being introduced into virtual try-on tasks after PF-AFN [6], they have been widely used [7; 28; 29; 30]. However, the FPN overlooks the issue of feature alignment, and directly adding pixels between upsampling and local featurescan cause a misalignment of contextual feature mappings, leading to incorrect garment warping and unrealistic results. In this paper, we introduce the Feature Alignment Selection Module (FASM) [31] into the feature extractor to address the problem of misaligned contextual feature mappings.