title: Inverse Virtual Try-On: Generating Multi-Category Product-Style Images
    from Clothed Individuals
summary: 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.
paper_authors._id: 6836dbe003f1a07f6f9fe0db
paper_authors.name: Davide Lobba
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-28T09:52:31.885Z
paper_authors._id: 65abccc9fd4261f5313f1c34
paper_authors.avatarUrl: /avatars/b4f09703e1fcd83ab3fc31a116dccde9.svg
paper_authors.fullname: Davide
paper_authors.type: user
paper_authors.user: davidelobba
paper_authors._id: 6836dbe003f1a07f6f9fe0dc
paper_authors.name: Fulvio Sanguigni
paper_authors._id: 6836dbe003f1a07f6f9fe0dd
paper_authors.name: Bin Ren
paper_authors._id: 6836dbe003f1a07f6f9fe0de
paper_authors.name: Marcella Cornia
paper_authors._id: 6836dbe003f1a07f6f9fe0df
paper_authors.name: Rita Cucchiara
paper_authors._id: 6836dbe003f1a07f6f9fe0e0
paper_authors.name: Nicu Sebe
paper_title: Inverse Virtual Try-On: Generating Multi-Category Product-Style Images
    from Clothed Individuals
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: DiT multimodal attention mechanism garment feature extraction multi-category virtual try-off alignment module