Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals

While virtual try-on (VTON) systems aim to render a garment onto a target person, this paper tackles the novel task of virtual try-off (VTOFF), which addresses the inverse problem: generating standardized product images from real-world photos of clothed individuals. Unlike VTON, which must resolve diverse pose and style variations, VTOFF benefits from a consistent output format, typically a flat, lay-down style, making it a promising tool for data generation and dataset enhancement. However, existing VTOFF approaches face two major limitations: (i) they are fundamentally constrained by their exclusive reliance on ambiguous visual information from the source image, and (ii) they frequently produce images with severely degraded details, preventing their use in practical applications. To overcome these challenges, we present Text-Enhanced MUlti-category Virtual Try-Off (TEMU-VTOFF), a novel architecture featuring a dual DiT-based backbone. To resolve visual ambiguity, our model leverages a modified multimodal attention mechanism that incorporates information from images, text, and masks, enabling robust feature extraction in a multi-category setting. To explicitly mitigate detail degradation, we propose an additional alignment module that refines the generated visual details to achieve high fidelity. Experiments on the VITON-HD and Dress Code datasets show that TEMU-VTOFF sets a new state-of-the-art on the VTOFF task, significantly improving both visual quality and fidelity to the target garments. Our code and models will be made publicly available.