AI Navigate

BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off

arXiv cs.CV / 3/11/2026

Ideas & Deep AnalysisModels & Research

Key Points

  • BridgeDiff is a novel diffusion-based framework designed to improve virtual try-off (VTOFF) by bridging the gap between human-worn garment images and canonical flat-garment representations.
  • It introduces the Garment Condition Bridge Module (GCBM) that generates a garment-cue representation capturing global appearance and semantic identity for robust detail inference even with partial visibility.
  • The Flat Structure Constraint Module (FSCM) incorporates explicit structural priors using Flat-Constraint Attention (FC-Attention) during denoising to enhance structural stability and consistency beyond text-only conditioning.
  • Extensive experiments demonstrate that BridgeDiff achieves state-of-the-art results, producing higher-quality flat-garment reconstructions that better preserve fine details and garment structure compared to prior methods.
  • This approach addresses common issues in VTOFF such as inconsistent completion and unstable garment structures by directly linking on-body appearance observations with flat layout synthesis.

Computer Science > Computer Vision and Pattern Recognition

arXiv:2603.09236 (cs)
[Submitted on 10 Mar 2026]

Title:BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off

View a PDF of the paper titled BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off, by Shuang Liu and 6 other authors
View PDF HTML (experimental)
Abstract:Virtual try-off (VTOFF) aims to recover canonical flat-garment representations from images of dressed persons for standardized display and downstream virtual try-on. Prior methods often treat VTOFF as direct image translation driven by local masks or text-only prompts, overlooking the gap between on-body appearances and flat layouts. This gap frequently leads to inconsistent completion in unobserved regions and unstable garment structure. We propose BridgeDiff, a diffusion-based framework that explicitly bridges human-centric observations and flat-garment synthesis through two complementary components. First, the Garment Condition Bridge Module (GCBM) builds a garment-cue representation that captures global appearance and semantic identity, enabling robust inference of continuous details under partial visibility. Second, the Flat Structure Constraint Module (FSCM) injects explicit flat-garment structural priors via Flat-Constraint Attention (FC-Attention) at selected denoising stages, improving structural stability beyond text-only conditioning. Extensive experiments on standard VTOFF benchmarks show that BridgeDiff achieves state-of-the-art performance, producing higher-quality flat-garment reconstructions while preserving fine-grained appearance and structural integrity.
Comments:
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.09236 [cs.CV]
  (or arXiv:2603.09236v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2603.09236
Focus to learn more
arXiv-issued DOI via DataCite

Submission history

From: Shuang Liu [view email]
[v1] Tue, 10 Mar 2026 06:12:32 UTC (9,614 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off, by Shuang Liu and 6 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source
Current browse context:
cs.CV
< prev   |   next >
Change to browse by:

References & Citations

export BibTeX citation Loading...

BibTeX formatted citation

×
Data provided by:

Bookmark

BibSonomy logo Reddit logo
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
Links to Code Toggle
Papers with Code (What is Papers with Code?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.