Image-based virtual try-on aims to fit an in-shop garment onto a clothed
person image. Garment warping, which aligns the target garment with the
corresponding body parts in the person image, is a crucial step in achieving
this goal. Existing methods often use multi-stage frameworks to handle clothes
warping, person body synthesis and tryon generation separately or rely on noisy
intermediate parser-based labels. We propose a novel single-stage framework
that implicitly learns the same without explicit multi-stage learning. Our
approach utilizes a novel semantic-contextual fusion attention module for
garment-person feature fusion, enabling efficient and realistic cloth warping
and body synthesis from target pose keypoints. By introducing a lightweight
linear attention framework that attends to garment regions and fuses multiple
sampled flow fields, we also address misalignment and artifacts present in
previous methods. To achieve simultaneous learning of warped garment and try-on
results, we introduce a Warped Cloth Learning Module. WCLM uses segmented
warped garments as ground truth, operating within a single-stage paradigm. Our
proposed approach significantly improves the quality and efficiency of virtual
try-on methods, providing users with a more reliable and realistic virtual
try-on experience. We evaluate our method on the VITON dataset and demonstrate
its state-of-the-art performance in terms of both qualitative and quantitative
metrics.

图像虚拟试穿是将店内服装与穿着衣物的人物图像相配。本文提出了一个新颖的单阶段框架，通过隐性学习实现衣物变形和人物生成，采用语义上下文融合注意力模块进行衣物 - 人物特征融合，从而实现高效且逼真的衣物变形和体型合成。通过引入轻量级线性注意力框架，处理以往方法中存在的对齐问题和伪影。为了同时学习变形后的服装和试穿结果，引入了一个变形服装学习模块。我们的方法在 VITON 数据集上进行评估，在定性和定量指标上展示了其卓越的性能。