title: TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction
    using Diffusion Models
summary: This paper introduces Virtual Try-Off (VTOFF), a novel task focused on
  generating standardized garment images from single photos of clothed
  individuals. Unlike traditional Virtual Try-On (VTON), which digitally dresses
  models, VTOFF aims to extract a canonical garment image, posing unique
  challenges in capturing garment shape, texture, and intricate patterns. This
  well-defined target makes VTOFF particularly effective for evaluating
  reconstruction fidelity in generative models. We present TryOffDiff, a model
  that adapts Stable Diffusion with SigLIP-based visual conditioning to ensure
  high fidelity and detail retention. Experiments on a modified VITON-HD dataset
  show that our approach outperforms baseline methods based on pose transfer and
  virtual try-on with fewer pre- and post-processing steps. Our analysis reveals
  that traditional image generation metrics inadequately assess reconstruction
  quality, prompting us to rely on DISTS for more accurate evaluation. Our
  results highlight the potential of VTOFF to enhance product imagery in
  e-commerce applications, advance generative model evaluation, and inspire
  future work on high-fidelity reconstruction. Demo, code, and models are
  available at: https://rizavelioglu.github.io/tryoffdiff/
paper_authors._id: 674818fd93aea005d45001bd
paper_authors.name: Riza Velioglu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-28T09:19:21.403Z
paper_authors._id: 63f222ecf4e30ffd2bd08405
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paper_authors.fullname: Riza Velioglu
paper_authors.type: user
paper_authors.user: rizavelioglu
paper_authors._id: 674818fd93aea005d45001be
paper_authors.name: Petra Bevandic
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-29T15:25:20.925Z
paper_authors._id: 66c7334bf486e7ed57231858
paper_authors.avatarUrl: /avatars/2f315177d64b683b32d7d4ef6a6c91eb.svg
paper_authors.fullname: Petra Bevandic
paper_authors.type: user
paper_authors.user: PetraBevandic
paper_authors._id: 674818fd93aea005d45001bf
paper_authors.name: Robin Chan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T09:46:16.168Z
paper_authors._id: 64491f1bb6ac93fe65149437
paper_authors.avatarUrl: /avatars/8553dd8a9e4972b90069b57998d8d8ca.svg
paper_authors.fullname: Robin Chan
paper_authors.type: user
paper_authors.user: robin-chan
paper_authors._id: 674818fd93aea005d45001c0
paper_authors.name: Barbara Hammer
paper_title: TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction
    using Diffusion Models
paper_summary: This paper introduces Virtual Try-Off (VTOFF), a novel task focused on
  generating standardized garment images from single photos of clothed
  individuals. Unlike traditional Virtual Try-On (VTON), which digitally dresses
  models, VTOFF aims to extract a canonical garment image, posing unique
  challenges in capturing garment shape, texture, and intricate patterns. This
  well-defined target makes VTOFF particularly effective for evaluating
  reconstruction fidelity in generative models. We present TryOffDiff, a model
  that adapts Stable Diffusion with SigLIP-based visual conditioning to ensure
  high fidelity and detail retention. Experiments on a modified VITON-HD dataset
  show that our approach outperforms baseline methods based on pose transfer and
  virtual try-on with fewer pre- and post-processing steps. Our analysis reveals
  that traditional image generation metrics inadequately assess reconstruction
  quality, prompting us to rely on DISTS for more accurate evaluation. Our
  results highlight the potential of VTOFF to enhance product imagery in
  e-commerce applications, advance generative model evaluation, and inspire
  future work on high-fidelity reconstruction. Demo, code, and models are
  available at: https://rizavelioglu.github.io/tryoffdiff/
paper_ai_keywords: Virtual Try-Off (VTOFF) Virtual Try-On (VTON) garment images garment shape garment texture intricate patterns TryOffDiff Stable Diffusion SigLIP-based visual conditioning high fidelity detail retention VITON-HD dataset pose transfer traditional image generation metrics DISTS product imagery e-commerce applications generative model evaluation high-fidelity reconstruction