# FA-VTON: A Feature Alignment-Based Model for Virtual Try-On
## 3 Twisted Analysis  
Current models have made various improvements in both garment warping and try-on image generation stages, but they have overlooked the importance of feature alignment in the extraction stage. We analyze the issues present in current virtual try-on results and explain the impact of feature alignment on garment warping and the final try-on result. Parser-based models, unlike parser-free ones, require multiple people or garment parsing results as inputs. Person representation types include pose key points, semantic segmentation, and DensePose segmentation [33]. Garment representation types include semantic segmentation and corresponding landmarks [34; 35]. The previous models simply concatenated the inputs from these different channel-wise modalities, without considering aligning them. To illustrate the importance of feature alignment in virtual try-on tasks, we take the results of FS-VTON as an example. We can visualize the final layer feature maps and obtain grayscale images by compressing the color channels. Unlike 3D virtual try-on, where garments can be modeled based on the body's spatial structure, 2D virtual try-on requires our model to correctly retain the front part of the garment shown after trying it on and remove the back fabric that will not appear in the result. As shown in Figure 2, in the first two rows, the try-on result retains the back fabric of the garment at the neckline, while in the third row, the detailed part of the neckline that should be retained is removed. In the feature maps in the second column, we can see that the model can only extract the rough outline of the garment and cannot distinguish complex clothing details. The garment warping module calculates the appearance flow based just on the extracted features to generate the warped garment. Consequently, misaligned areas in the feature extraction stage result in the erroneous retention or removal of areas in the warped garment in the third column, thereby affecting the final try-on result. Then, we analyze the influence of the person feature extractor on the try-on results, as shown in Figure 3. We classify the upper body, including the arms and neck, as the deformable garment regions. Features like the face and other areas that do not require deformation, such as the hands and lower body, are identified as non-deformable regions. We expect the person feature extractor to accurately identify the deformable garment regions. This should exclude the influence of existing clothing from the original person image and guide the subsequent garment deformation. Parser-free methods struggle to exclude interference from existing garments during input. Therefore, the person feature extractor must accurately identify real human body boundaries to determine regions to be retained and warped. Figure 2: The influence of garment feature extraction on try-on results. We classify the twists caused by existing garment feature extraction into two categories. The first category is twists from unclear edges of human images. In the first row in the trial results, excessive twisting occurs at the shoulders. This happens because the person feature extractor fails to learn clear edge features, leading to an ambiguous deformable garment region. The garment warping module requires guidance from the human feature map for deformation. Thus, the appearance flow struggles to learn the accurate position of corresponding pixels. Under multiple spatial transformations, this deviation is magnified, causing twists at the edges. The second category is twists from the inability to eliminate the influence of existing garments. In the second row, the try-on image retains the V-neck design of the original garment, while the target garment has a round neck. The feature extractor is disturbed by the existing clothing, incorrectly retaining the skin at the neckline of the original image. As a result, this leads the garment to deform incorrectly, ultimately impacting the try-on outcome.