Layering Virtual Try-On

In the real world, fashion is about layering: adding a jacketover a shirt, or a sequence of adding and removing layers, rather thanjust a single-layer swap. This fundamental real-world task remains achallenge in existing Virtual Try-On (VTON) methods, which excel atsingle-layer replacement but are not designed to layer or de-layer anexisting outfit. This paper proposes Layering Virtual Try-On (LVTON),a layering benchmark and method that preserves an existing outfit whileenabling sequential layering. We find that current VTON paradigms arefundamentally ill-equipped for LVTON, as their reliance on cloth-agnosticrepresentations and single-item datasets discards essential layering context.Our key insight is that the LVTON challenge must be disentangled intotwo distinct competencies: (1) General VTON Priors (e.g., deformation,identity preservation) and (2) Specific Layering Knowledge (e.g., layeringorder and occlusion reasoning). First, our model obtains general VTONpriors by being trained on data produced by an automatic data generationpipeline that synthesizes samples from fashion videos via segmentationand inpainting. Second, the model is fine-tuned on a small, dedicatedLVTON dataset to learn the layering logic. Our method achieves state-of-the-art results on our LVTON benchmark and demonstrates superiorgeneralizability on traditional VTON benchmarks, setting new state-of-the-art results when fine-tuned and exhibiting zero-shot capabilities.