Summary of Tryoffdiff: Virtual-try-off Via High-fidelity Garment Reconstruction Using Diffusion Models, by Riza Velioglu et al.
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
by Riza Velioglu, Petra Bevandic, Robin Chan, Barbara Hammer
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Virtual Try-Off (VTOFF), a novel task in generative models that focuses 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. The authors 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 their approach outperforms baseline methods based on pose transfer and virtual try-on with fewer pre- and post-processing steps. The authors also highlight the limitations of traditional image generation metrics, which inadequately assess reconstruction quality, and instead rely on DISTS for more accurate evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to generate pictures of people wearing clothes from just one photo of them. It’s called Virtual Try-Off (VTOFF) and it’s different from other techniques that just put digital clothes on models. VTOFF wants to create a standard picture of the garment, which is tricky because clothes have shapes, textures, and patterns that are hard to capture. The authors created a model called TryOffDiff that uses Stable Diffusion with SigLIP-based visual conditioning to make sure the pictures are accurate and detailed. They tested it on a special dataset and showed that their method works better than others with fewer steps. They also found that usual ways of measuring picture quality aren’t good enough, so they used DISTS instead. |
Keywords
» Artificial intelligence » Diffusion » Image generation
