Garment reconstruction from a single-view image based on pixel-aligned implicit function

3D garment reconstruction has a wide range of applications in apparel design, digital human body, and virtual try-on. Reconstructing 3D shapes from single-view images is a completely undefined and challenging problem. Recent single-view methods require only single-view images of static or dynamic objects and mine the potential multi-view information in single-view images by statistical, geometric, and physical prior knowledge, which is tedious to obtain. In this paper, we use an implicit function of pixel alignment represented by a neural network to correlate 2D image pixels with the corresponding 3D clothing information for end-to-end training without any relevant prior model. The qualitative and quantitative analysis of the experimental results showed that our results reduced the relative error by an average of 2.6% and the chamfer distance by 2.37% compared to the previous method. Experiments show that our model can reasonably reconstruct the 3D model of garments from their collections of single-view images. Our method can not only capture the overall geometry of the garment but also extract the tiny but important wrinkle details of the fabric. Even with low-resolution input images, our model still achieves available results. In addition, experiments show that the reconstructed 3D models of garments can be used for texture migration and virtual fitting.