Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models

• URL: http://arxiv.org/abs/2403.07371v3
• Date: Wed, 17 Jul 2024 06:50:47 GMT
• Title: Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models
• Authors: Phuong Dam, Jihoon Jeong, Anh Tran, Daeyoung Kim,
• Abstract summary: This study emphasizes the challenges of preserving intricate texture details and distinctive features of the target person and the clothes in various scenarios. Various existing approaches are explored, highlighting the limitations and unresolved aspects. It then proposes a novel diffusion-based solution that addresses garment texture preservation and user identity retention during virtual try-on.
• Score: 4.038493506169702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study discusses the critical issues of Virtual Try-On in contemporary e-commerce and the prospective metaverse, emphasizing the challenges of preserving intricate texture details and distinctive features of the target person and the clothes in various scenarios, such as clothing texture and identity characteristics like tattoos or accessories. In addition to the fidelity of the synthesized images, the efficiency of the synthesis process presents a significant hurdle. Various existing approaches are explored, highlighting the limitations and unresolved aspects, e.g., identity information omission, uncontrollable artifacts, and low synthesis speed. It then proposes a novel diffusion-based solution that addresses garment texture preservation and user identity retention during virtual try-on. The proposed network comprises two primary modules - a warping module aligning clothing with individual features and a try-on module refining the attire and generating missing parts integrated with a mask-aware post-processing technique ensuring the integrity of the individual's identity. It demonstrates impressive results, surpassing the state-of-the-art in speed by nearly 20 times during inference, with superior fidelity in qualitative assessments. Quantitative evaluations confirm comparable performance with the recent SOTA method on the VITON-HD and Dresscode datasets. We named our model Fast and Identity Preservation Virtual TryON (FIP-VITON).

Related papers

• Hierarchical Cross-Attention Network for Virtual Try-On [59.50297858307268]
  We present an innovative solution for the challenges of the virtual try-on task: our novel Hierarchical Cross-Attention Network (HCANet) HCANet is crafted with two primary stages: geometric matching and try-on, each playing a crucial role in delivering realistic virtual try-on outcomes. A key feature of HCANet is the incorporation of a novel Hierarchical Cross-Attention (HCA) block into both stages, enabling the effective capture of long-range correlations between individual and clothing modalities.
  arXiv  Detail & Related papers  (2024-11-23T12:39:58Z)
• 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)
• AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario [50.62711489896909]
  AnyFit surpasses all baselines on high-resolution benchmarks and real-world data by a large gap. AnyFit's impressive performance on high-fidelity virtual try-ons in any scenario from any image, paves a new path for future research within the fashion community.
  arXiv  Detail & Related papers  (2024-05-28T13:33:08Z)
• Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification [13.709863134725335]
  Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify the target person in more realistic surveillance scenarios, where pedestrians usually change their clothing. Despite great progress, limited cloth-changing training samples in existing CC-ReID datasets still prevent the model from adequately learning cloth-irrelevant features. We propose an Identity-aware Dual-constraint Network (IDNet) for the CC-ReID task.
  arXiv  Detail & Related papers  (2024-03-13T05:46:36Z)
• 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)
• Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention [55.33017432880408]
  This paper proposes the use of Parallel Visual Attention (PVA) in conjunction with diffusion models to improve inpainting results. We train the added attention modules and identity encoder on CelebAHQ-IDI, a dataset proposed for identity-preserving face inpainting. Experiments demonstrate that PVA attains unparalleled identity resemblance in both face inpainting and face inpainting with language guidance tasks.
  arXiv  Detail & Related papers  (2023-12-06T15:39:03Z)
• 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)
• ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors [13.977100716044104]
  Image-based virtual try-on involves synthesizing convincing images of a model wearing a particular garment. Recent methods involve a two stage process: i.) warping of the garment to align with the model ii. The lack of geometric information about the model or the garment often results in improper rendering of granular details. We propose ZFlow, an end-to-end framework, which seeks to alleviate these concerns.
  arXiv  Detail & Related papers  (2021-09-14T22:41:14Z)
• 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)

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.