Enhancing Person-to-Person Virtual Try-On with Multi-Garment Virtual Try-Off

• URL: http://arxiv.org/abs/2504.13078v1
• Date: Thu, 17 Apr 2025 16:45:18 GMT
• Title: Enhancing Person-to-Person Virtual Try-On with Multi-Garment Virtual Try-Off
• Authors: Riza Velioglu, Petra Bevandic, Robin Chan, Barbara Hammer,
• Abstract summary: Computer vision is transforming fashion through Virtual Try-On and Virtual Try-Off.<n>VTON generates images of a person in a specified garment using a target photo and a standardized garment image.<n>VTOFF, on the other hand, extracts standardized garment images from clothed individuals.<n>We introduce TryOffDiff, a diffusion-based VTOFF model.
• Score: 8.158200403139196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer vision is transforming fashion through Virtual Try-On (VTON) and Virtual Try-Off (VTOFF). VTON generates images of a person in a specified garment using a target photo and a standardized garment image, while a more challenging variant, Person-to-Person Virtual Try-On (p2p-VTON), uses a photo of another person wearing the garment. VTOFF, on the other hand, extracts standardized garment images from clothed individuals. We introduce TryOffDiff, a diffusion-based VTOFF model. Built on a latent diffusion framework with SigLIP image conditioning, it effectively captures garment properties like texture, shape, and patterns. TryOffDiff achieves state-of-the-art results on VITON-HD and strong performance on DressCode dataset, covering upper-body, lower-body, and dresses. Enhanced with class-specific embeddings, it pioneers multi-garment VTOFF, the first of its kind. When paired with VTON models, it improves p2p-VTON by minimizing unwanted attribute transfer, such as skin color. Code is available at: https://rizavelioglu.github.io/tryoffdiff/

Related papers

• Limb-Aware Virtual Try-On Network with Progressive Clothing Warping [64.84181064722084]
  Image-based virtual try-on aims to transfer an in-shop clothing image to a person image. Most existing methods adopt a single global deformation to perform clothing warping directly. We propose Limb-aware Virtual Try-on Network named PL-VTON, which performs fine-grained clothing warping progressively.
  arXiv  Detail & Related papers  (2025-03-18T09:52:41Z)
• MFP-VTON: Enhancing Mask-Free Person-to-Person Virtual Try-On via Diffusion Transformer [5.844515709826269]
  Garment-to-person virtual try-on (VTON) aims to generate fitting images of a person wearing a reference garment. To improve ease of use, we propose a Mask-Free framework for Person-to-Person VTON. Our model excels in both person-to-person and garment-to-person VTON tasks, generating high-fidelity fitting images.
  arXiv  Detail & Related papers  (2025-02-03T18:56:24Z)
• Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks [31.461116368933165]
  Image-based virtual try-on (VTON) aims to generate a virtual try-on result by transferring an input garment onto a target person's image. The scarcity of paired garment-model data makes it challenging for existing methods to achieve high generalization and quality in VTON. We propose Any2AnyTryon, which can generate try-on results based on different textual instructions and model garment images.
  arXiv  Detail & Related papers  (2025-01-27T09:33:23Z)
• TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models [8.158200403139196]
  This paper introduces Virtual Try-Off (VTOFF), a novel task focused on generating standardized garment images from single photos of clothed individuals.<n>We present TryOffDiff, a model that adapts Stable Diffusion with SigLIP-based visual conditioning to ensure high fidelity and detail retention.<n>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.
  arXiv  Detail & Related papers  (2024-11-27T13:53:09Z)
• Improving Virtual Try-On with Garment-focused Diffusion Models [91.95830983115474]
  Diffusion models have led to the revolutionizing of generative modeling in numerous image synthesis tasks. We shape a new Diffusion model, namely GarDiff, which triggers the garment-focused diffusion process. Experiments on VITON-HD and DressCode datasets demonstrate the superiority of our GarDiff when compared to state-of-the-art VTON approaches.
  arXiv  Detail & Related papers  (2024-09-12T17:55:11Z)
• OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person [38.69239957207417]
  OutfitAnyone generates high-fidelity and detail-consistent images for virtual clothing trials. It distinguishes itself with scalability-ulating factors such as pose, body shape and broad applicability. OutfitAnyone's performance in diverse scenarios underscores its utility and readiness for real-world deployment.
  arXiv  Detail & Related papers  (2024-07-23T07:04:42Z)
• IMAGDressing-v1: Customizable Virtual Dressing [58.44155202253754]
  IMAGDressing-v1 is a virtual dressing task that generates freely editable human images with fixed garments and optional conditions. IMAGDressing-v1 incorporates a garment UNet that captures semantic features from CLIP and texture features from VAE. We present a hybrid attention module, including a frozen self-attention and a trainable cross-attention, to integrate garment features from the garment UNet into a frozen denoising UNet.
  arXiv  Detail & Related papers  (2024-07-17T16:26:30Z)
• MV-VTON: Multi-View Virtual Try-On with Diffusion Models [91.71150387151042]
  The goal of image-based virtual try-on is to generate an image of the target person naturally wearing the given clothing.<n>Existing methods solely focus on the frontal try-on using the frontal clothing.<n>We introduce Multi-View Virtual Try-ON (MV-VTON), which aims to reconstruct the dressing results from multiple views using the given clothes.
  arXiv  Detail & Related papers  (2024-04-26T12:27:57Z)
• Improving Diffusion Models for Authentic Virtual Try-on in the Wild [53.96244595495942]
  This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment. We propose a novel diffusion model that improves garment fidelity and generates authentic virtual try-on images. We present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity.
  arXiv  Detail & Related papers  (2024-03-08T08:12:18Z)
• Learning Garment DensePose for Robust Warping in Virtual Try-On [72.13052519560462]
  We propose a robust warping method for virtual try-on based on a learned garment DensePose. Our method achieves the state-of-the-art equivalent on virtual try-on benchmarks.
  arXiv  Detail & Related papers  (2023-03-30T20:02:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.