This paper presents a novel method for reconstructing 3D garment models from
a single image of a posed user. Previous studies that have primarily focused on
accurately reconstructing garment geometries to match the input garment image
may often result in unnatural-looking garments when deformed for new poses. To
overcome this limitation, our approach takes a different approach by inferring
the fundamental shape of the garment through sewing patterns from a single
image, rather than directly reconstructing 3D garments. Our method consists of
two stages. Firstly, given a single image of a posed user, it predicts the
garment image worn on a T-pose, representing the baseline form of the garment.
Then, it estimates the sewing pattern parameters based on the T-pose garment
image. By simulating the stitching and draping of the sewing pattern using
physics simulation, we can generate 3D garments that can adaptively deform to
arbitrary poses. The effectiveness of our method is validated through ablation
studies on the major components and a comparison with other approaches.

从单张图像中重建三维服装模型的一种新方法，通过推断单张图像中的缝制图案来获得服装的基本形状，然后使用物理模拟来生成能够适应任意姿势的三维服装。

SPnet：从单张图像估计服装缝制图案

SPnet: Estimating Garment Sewing Patterns from a Single Image

High-fidelity clothing reconstruction is the key to achieving photorealism in
a wide range of applications including human digitization, virtual try-on, etc.
Recent advances in learning-based approaches have accomplished unprecedented
accuracy in recovering unclothed human shape and pose from single images,
thanks to the availability of powerful statistical models, e.g. SMPL, learned
from a large number of body scans. In contrast, modeling and recovering clothed
human and 3D garments remains notoriously difficult, mostly due to the lack of
large-scale clothing models available for the research community. We propose to
fill this gap by introducing Deep Fashion3D, the largest collection to date of
3D garment models, with the goal of establishing a novel benchmark and dataset
for the evaluation of image-based garment reconstruction systems. Deep
Fashion3D contains 2078 models reconstructed from real garments, which covers
10 different categories and 563 garment instances. It provides rich annotations
including 3D feature lines, 3D body pose and the corresponded multi-view real
images. In addition, each garment is randomly posed to enhance the variety of
real clothing deformations. To demonstrate the advantage of Deep Fashion3D, we
propose a novel baseline approach for single-view garment reconstruction, which
leverages the merits of both mesh and implicit representations. A novel
adaptable template is proposed to enable the learning of all types of clothing
in a single network. Extensive experiments have been conducted on the proposed
dataset to verify its significance and usefulness. We will make Deep Fashion3D
publicly available upon publication.

Deep Fashion3D 是迄今为止最大的 3D 服装模型集合，包含 2078 个模型和丰富的注释信息，并引入了一种新的单视角服装重建方法。