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 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.
6836dbe003f1a07f6f9fe0db
Davide Lobba
extracted_confirmed
2025-05-28T09:52:31.885Z
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/avatars/b4f09703e1fcd83ab3fc31a116dccde9.svg
Davide
user
davidelobba
6836dbe003f1a07f6f9fe0dc
Fulvio Sanguigni
6836dbe003f1a07f6f9fe0dd
Bin Ren
6836dbe003f1a07f6f9fe0de
Marcella Cornia
6836dbe003f1a07f6f9fe0df
Rita Cucchiara
6836dbe003f1a07f6f9fe0e0
Nicu Sebe
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 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.
TEMU-VTOFF, a novel multi-modal architecture using DiT and multimodal attention, achieves state-of-the-art performance on virtual try-off tasks by disentangling garment features and handling multiple garment categories.
DiT multimodal attention mechanism garment feature extraction multi-category virtual try-off alignment module