Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP)

• URL: http://arxiv.org/abs/2111.08270v1
• Date: Tue, 16 Nov 2021 07:40:16 GMT
• Title: Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP)
• Authors: Taewon Kang, Sunghyun Park, Seunghwan Choi, Jaegul Choo
• Abstract summary: VITON-CROP synthesizes images more robustly when integrated with random crop augmentation compared to the existing state-of-the-art virtual try-on models. In the experiments, we demonstrate that VITON-CROP is superior to VITON-HD both qualitatively and quantitatively.
• Score: 18.347532903864597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-based virtual try-on provides the capacity to transfer a clothing item onto a photo of a given person, which is usually accomplished by warping the item to a given human pose and adjusting the warped item to the person. However, the results of real-world synthetic images (e.g., selfies) from the previous method is not realistic because of the limitations which result in the neck being misrepresented and significant changes to the style of the garment. To address these challenges, we propose a novel method to solve this unique issue, called VITON-CROP. VITON-CROP synthesizes images more robustly when integrated with random crop augmentation compared to the existing state-of-the-art virtual try-on models. In the experiments, we demonstrate that VITON-CROP is superior to VITON-HD both qualitatively and quantitatively.

Related papers

• 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. Existing methods solely focus on the frontal try-on using the frontal clothing. 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)
• Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On [29.217423805933727]
  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.
  arXiv  Detail & Related papers  (2024-04-01T12:43:22Z)
• 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)
• 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)
• Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur [68.24599239479326]
  We develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Our model surpasses state-of-the-art point-based methods for novel view synthesis.
  arXiv  Detail & Related papers  (2023-04-25T08:36:33Z)
• OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup [79.3118064406151]
  Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes) Prior methods successfully preserve the character of clothing images. Occlusion remains a pernicious effect for realistic virtual try-on.
  arXiv  Detail & Related papers  (2023-01-03T06:29:11Z)
• C-VTON: Context-Driven Image-Based Virtual Try-On Network [1.0832844764942349]
  We propose a Context-Driven Virtual Try-On Network (C-VTON) that convincingly transfers selected clothing items to the target subjects. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when the final try-on result.
  arXiv  Detail & Related papers  (2022-12-08T17:56:34Z)
• Cloth Interactive Transformer for Virtual Try-On [106.21605249649957]
  We propose a novel two-stage cloth interactive transformer (CIT) method for the virtual try-on task. In the first stage, we design a CIT matching block, aiming to precisely capture the long-range correlations between the cloth-agnostic person information and the in-shop cloth information. In the second stage, we put forth a CIT reasoning block for establishing global mutual interactive dependencies among person representation, the warped clothing item, and the corresponding warped cloth mask.
  arXiv  Detail & Related papers  (2021-04-12T14:45:32Z)
• VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization [18.347532903864597]
  We propose a novel virtual try-on method called VITON-HD that successfully synthesizes 1024x768 virtual try-on images. We show that VITON-HD highly sur-passes the baselines in terms of synthesized image quality both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2021-03-31T07:52:41Z)
• Disentangled Cycle Consistency for Highly-realistic Virtual Try-On [34.97658860425598]
  Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. Existing methods formulate virtual try-on as either in-painting or cycle consistency. We propose a Disentangled Cycle-consistency Try-On Network (DCTON)
  arXiv  Detail & Related papers  (2021-03-17T07:18:55Z)

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.