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

• URL: http://arxiv.org/abs/2505.21062v1
• Date: Tue, 27 May 2025 11:47:51 GMT
• Title: Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals
• Authors: Davide Lobba, Fulvio Sanguigni, Bin Ren, Marcella Cornia, Rita Cucchiara, Nicu Sebe,
• Abstract summary: We present Text-Enhanced MUlti-category Virtual Try-Off (TEMU-VTOFF)<n>Our architecture is designed to receive garment information from multiple modalities like images, text, and masks to work in a multi-category setting.<n> Experiments on VITON-HD and Dress Code datasets show that TEMU-VTOFF sets a new state-of-the-art on the VTOFF task.
• Score: 76.96387718150542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While virtual try-on (VTON) systems aim to render a garment onto a target person image, this paper tackles the novel task of virtual try-off (VTOFF), which addresses the inverse problem: generating standardized product images of garments from real-world photos of clothed individuals. Unlike VTON, which must resolve diverse pose and style variations, VTOFF benefits from a consistent and well-defined output format -- typically a flat, lay-down-style representation of the garment -- making it a promising tool for data generation and dataset enhancement. However, existing VTOFF approaches face two major limitations: (i) difficulty in disentangling garment features from occlusions and complex poses, often leading to visual artifacts, and (ii) restricted applicability to single-category garments (e.g., upper-body clothes only), limiting generalization. To address these challenges, we present Text-Enhanced MUlti-category Virtual Try-Off (TEMU-VTOFF), a novel architecture featuring a dual DiT-based backbone with a modified multimodal attention mechanism for robust garment feature extraction. Our architecture is designed to receive garment information from multiple modalities like images, text, and masks to work in a multi-category setting. Finally, we propose an additional alignment module to further refine the generated visual details. Experiments on 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.

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