This paper investigates occlusion issues in virtual try-on (VTON) tasks and proposes a novel mask-free framework that effectively addresses inherent and acquired occlusions through background pre-replacement and covering-and-eliminating operations.

We propose a data removal method using factor decorrelation and loss perturbation to boost model robustness and accuracy, achieving strong performance on five benchmarks even under significant distribution shifts.