# FA-VTON: A Feature Alignment-Based Model for Virtual Try-On
## 1 Introduction  
With the surge in online shopping, virtual try-on technology has emerged as a highly esteemed innovative solution, aiming to enhance users' try-on experience in response to the inherent challenge of not being able to physically try on garments while shopping online. Currently, research on virtual try-ons can be broadly categorized into two main types: based on 3D models [1; 2; 3] and based on 2D images [4; 5; 6; 7; 8; 9; 10; 11]. Virtual try-on technology based on 3D methods requires 3D parametric human body models [12]. Acquiring data for 3D models presents challenges due to its difficulty and cost, which in turn restricts its accessibility and complicates its promotion and application. Conversely, image-based models offer a more straightforward and cost-effective means of obtaining image data, thereby expanding their potential applications. Consequently, they have garnered growing interest from researchers. While 2D virtual try-on technology offers reduced complexity compared to its 3D counterpart and can yield satisfactory results, it still faces limitations. Many existing methods overlook the crucial aspect of feature alignment during the extraction process. Low-quality multi-scale features often lead to undesirable warping effects. For example, blurred edges in person features may cause garments to warp disproportionately, resulting in unclear try-on images or generating artifacts that adversely affect the try-on experience. As shown in Figure 1, we choose the current SOTA parser-free model FS-VTON [7] for comparison. In the second column of each sub-figure, we can see that the feature maps extracted by the FS-VTON model are very blurry, especially at the boundaries of objects. The third column shows the features extracted by our model, which are significantly higher in quality than those of FS-VTON, especially in terms of garment patterns and boundaries. In virtual try-on tasks, we not only want warped garments to retain as much original information as possible but also need to remove parts that did not appear in the final try-on image. However, existing methods struggle to distinguish garment boundaries, remove necessary details, like complex texture structures, or retain unnecessary parts, such as the back fabric of garments. Furthermore, existing models fail to consider the weighted processing of channel information during try-on image generation. The generated results may not adequately enhance retained garment features and fail to suppress incorrectly warped garment features, resulting in artifacts. Finally, virtual try-on models typically combine various person or garment parsing results as prior knowledge inputs. Most parser-free models simply minimize point-wise differences, neglecting the impact of semantic information from different channels in teacher models. To handle the above issues, we propose a feature alignment-based model (FA-VTON) without relying on people or garment parsing. Firstly, in the feature extraction stage, we introduce a Feature Alignment Selection Module (FASM), which consists of two parts: a Feature Alignment Module (FAM) and a Feature Selection Module (FSM). The FAM effectively learns pixel transformation offsets to contextually align upsampled higher-level features, allowing the model to learn more precise boundaries of both the person and garment image. It helps guide accurate garment warping. The FSM selectively retains important features and reduces irrelevant feature interference. Secondly, we utilize the ECA mechanism in the try-on image generation stage. It learns effective channel attention and avoids losses caused by dimensionality reduction. The try-on image generation module effectively mitigates errors resulting from incorrect parsing or excessive garment deformation, improving the quality of generated images. Lastly, we adopt a knowledge distillation framework to train our parser-free virtual try-on model and utilize a channel-wise knowledge distillation method to learn significant feature information from the teacher network.