Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On

• URL: http://arxiv.org/abs/2404.01089v1
• Date: Mon, 1 Apr 2024 12:43:22 GMT
• Title: Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On
• Authors: Xu Yang, Changxing Ding, Zhibin Hong, Junhao Huang, Jin Tao, Xiangmin Xu,
• Abstract summary: Diffusion model-based approaches have recently become popular, as they are excellent at image synthesis tasks. We propose an Texture-Preserving Diffusion (TPD) model for virtual try-on, which enhances the fidelity of the results. Second, we propose a novel diffusion-based method that predicts a precise inpainting mask based on the person and reference garment images.
• Score: 29.217423805933727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-based virtual try-on is an increasingly important task for online shopping. It aims to synthesize images of a specific person wearing a specified garment. Diffusion model-based approaches have recently become popular, as they are excellent at image synthesis tasks. However, these approaches usually employ additional image encoders and rely on the cross-attention mechanism for texture transfer from the garment to the person image, which affects the try-on's efficiency and fidelity. To address these issues, we propose an Texture-Preserving Diffusion (TPD) model for virtual try-on, which enhances the fidelity of the results and introduces no additional image encoders. Accordingly, we make contributions from two aspects. First, we propose to concatenate the masked person and reference garment images along the spatial dimension and utilize the resulting image as the input for the diffusion model's denoising UNet. This enables the original self-attention layers contained in the diffusion model to achieve efficient and accurate texture transfer. Second, we propose a novel diffusion-based method that predicts a precise inpainting mask based on the person and reference garment images, further enhancing the reliability of the try-on results. In addition, we integrate mask prediction and image synthesis into a single compact model. The experimental results show that our approach can be applied to various try-on tasks, e.g., garment-to-person and person-to-person try-ons, and significantly outperforms state-of-the-art methods on popular VITON, VITON-HD databases.

Related papers

• 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)
• 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)
• Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis [65.7968515029306]
  We propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for Pose-Guided Person Image Synthesis (PGPIS) A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt.
  arXiv  Detail & Related papers  (2024-02-28T06:07:07Z)
• WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on [81.15988741258683]
  Image-based Virtual Try-On (VITON) aims to transfer an in-shop garment image onto a target person. Current methods often overlook the synthesis quality around the garment-skin boundary and realistic effects like wrinkles and shadows on the warped garments. We propose WarpDiffusion, which bridges the warping-based and diffusion-based paradigms via a novel informative and local garment feature attention mechanism.
  arXiv  Detail & Related papers  (2023-12-06T18:34:32Z)
• StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On [35.227896906556026]
  Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process.
  arXiv  Detail & Related papers  (2023-12-04T08:27:59Z)
• MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask [84.84034179136458]
  A crucial factor leading to the text-image mismatch issue is the inadequate cross-modality relation learning. We propose an adaptive mask, which is conditioned on the attention maps and the prompt embeddings, to dynamically adjust the contribution of each text token to the image features. Our method, termed MaskDiffusion, is training-free and hot-pluggable for popular pre-trained diffusion models.
  arXiv  Detail & Related papers  (2023-09-08T15:53:37Z)
• Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow [24.187109053871833]
  Virtual try-on is a critical image synthesis task that aims to transfer clothes from one image to another while preserving the details of both humans and clothes. We propose an exemplar-based inpainting approach that leverages a warping module to guide the diffusion model's generation effectively. Our approach, namely Diffusion-based Conditional Inpainting for Virtual Try-ON (DCI-VTON), effectively utilizes the power of the diffusion model.
  arXiv  Detail & Related papers  (2023-08-11T12:23:09Z)
• Person Image Synthesis via Denoising Diffusion Model [116.34633988927429]
  We show how denoising diffusion models can be applied for high-fidelity person image synthesis. Our results on two large-scale benchmarks and a user study demonstrate the photorealism of our proposed approach under challenging scenarios.
  arXiv  Detail & Related papers  (2022-11-22T18:59:50Z)

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.