ToWo-VTON: Take-Off Wear-On Virtual Try-On via Topology-aware Decoupling

Virtual Try-On (VTON) aims to synthesize garment transfer without physical trials. Although recent methods perform well on upper-body garments, they often fail in lower-body and full-body scenarios due to complex occlusions and inadequate modeling of body structure. To address this limitation, we introduce ToWo (Take-Off Wear-On), a plug-in two-stage paradigm that decouples body topology reconstruction from garment synthesis. Take-Off first recovers an occlusion-free body representation, and Wear-On then synthesizes the target garment conditioned on this structural prior. This decoupling improves garment placement and robustness, especially in challenging lower-body and full-body settings with large silhouette variations and cross-category transformations. Built on this paradigm, we propose ToWo-VTON, a unified topology-aware framework with structure-aware supervision. We construct a new Body-Fitting Dress-Code (BFDC) dataset via body-aware diffusion and VLM-based filtering to provide topology-consistent supervision across upper-, lower-, and full-body cases, particularly benefiting scenarios with severe occlusions. To demonstrate generality, we instantiate ToWo-VTON on both diffusion-based and MLLM-guided backbones. Diffusion models adopt a plug-in conditioning strategy with BFDC fine-tuning, while MLLM-guided models incorporate Multimodal Alignment Blocks (MABs) for semantic–topology alignment and Garment Boosting Attention (GBA) for detail enhancement. Extensive experiments demonstrate that ToWo-VTON achieves state-of-the-art performance, reducing KID by 26.1% and 51.5% across diffusion-based and MLLM-guided backbones, respectively. It also produces more coherent and perceptually realistic results across diverse garment categories.