通过关键点对齐实现高保真虚拟试穿，细节还原更精准。
From Keypoints to Realism: A Realistic and Accurate Virtual Try-on Network from 2D Images
- 基于预测关键点进行精确形变，对齐目标服装与人体姿态。
- 生成图像保留服装整体形状与纹理特征，提升视觉真实感。
- 适合需要高精度服装还原的电商、设计等场景应用。
基于图像的虚拟试穿旨在生成个体穿着目标服装的真实感图像，确保姿态、体型及服装特征准确保留。现有方法常难以有效复现目标服装的精细细节，且在新场景下泛化能力不足。本文提出的方法首先完全移除人物初始服装，随后利用预测的关键点执行精确形变，将目标服装与人体结构及姿态完全对齐。基于形变后的服装，更准确地预测身体分割图。接着，通过一种对齐感知的分割归一化方法，消除形变服装与分割图中预测服装区域之间的错位部分。最后，生成器输出高质量最终图像，重建目标服装的精确特征，包括整体轮廓和纹理。该方法强调保持服装特征并提升对多种姿态的适应性，为多样化应用场景提供更好泛化能力。
原文摘要 · Abstract (English)
The aim of image-based virtual try-on is to generate realistic images of individuals wearing target garments, ensuring that the pose, body shape and characteristics of the target garment are accurately preserved. Existing methods often fail to reproduce the fine details of target garments effectively and lack generalizability to new scenarios. In the proposed method, the person's initial garment is completely removed. Subsequently, a precise warping is performed using the predicted keypoints to fully align the target garment with the body structure and pose of the individual. Based on the warped garment, a body segmentation map is more accurately predicted. Then, using an alignment-aware segment normalization, the misaligned areas between the warped garment and the predicted garment region in the segmentation map are removed. Finally, the generator produces the final image with high visual quality, reconstructing the precise characteristics of the target garment, including its overall shape and texture. This approach emphasizes preserving garment characteristics and improving adaptability to various poses, providing better generalization for diverse applications.
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