ITVTON:Virtual Try-On Diffusion Transformer Model Based on Integrated Image and Text

• URL: http://arxiv.org/abs/2501.16757v1
• Date: Tue, 28 Jan 2025 07:24:15 GMT
• Title: ITVTON:Virtual Try-On Diffusion Transformer Model Based on Integrated Image and Text
• Authors: Haifeng Ni,
• Abstract summary: We introduce ITVTON, a method that enhances clothing-character interactions by combining clothing and character images along spatial channels as inputs. We incorporate integrated textual descriptions from multiple images to boost the realism of the generated visual effects. In experiments, ITVTON outperforms baseline methods both qualitatively and quantitatively.
• Score: 0.0
• License:
• Abstract: Recent advancements in virtual fitting for characters and clothing have leveraged diffusion models to improve the realism of garment fitting. However, challenges remain in handling complex scenes and poses, which can result in unnatural garment fitting and poorly rendered intricate patterns. In this work, we introduce ITVTON, a novel method that enhances clothing-character interactions by combining clothing and character images along spatial channels as inputs, thereby improving fitting accuracy for the inpainting model. Additionally, we incorporate integrated textual descriptions from multiple images to boost the realism of the generated visual effects. To optimize computational efficiency, we limit training to the attention parameters within a single diffusion transformer (Single-DiT) block. To more rigorously address the complexities of real-world scenarios, we curated training samples from the IGPair dataset, thereby enhancing ITVTON's performance across diverse environments. Extensive experiments demonstrate that ITVTON outperforms baseline methods both qualitatively and quantitatively, setting a new standard for virtual fitting tasks.

Related papers

• TryOffAnyone: Tiled Cloth Generation from a Dressed Person [1.4732811715354452]
  High-fidelity tiled garment images are essential for personalized recommendations, outfit composition, and virtual try-on systems. We propose a novel approach utilizing a fine-tuned StableDiffusion model. Our method features a streamlined single-stage network design, which integrates garmentspecific masks to isolate and process target clothing items effectively.
  arXiv  Detail & Related papers  (2024-12-11T17:41:53Z)
• 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)
• Self-Supervised Vision Transformer for Enhanced Virtual Clothes Try-On [21.422611451978863]
  We introduce an innovative approach for virtual clothes try-on, utilizing a self-supervised Vision Transformer (ViT) and a diffusion model. Our method emphasizes detail enhancement by contrasting local clothing image embeddings, generated by ViT, with their global counterparts. The experimental results showcase substantial advancements in the realism and precision of details in virtual try-on experiences.
  arXiv  Detail & Related papers  (2024-06-15T07:46:22Z)
• 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)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models [4.038493506169702]
  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.
  arXiv  Detail & Related papers  (2024-03-12T07:15:29Z)
• Federated Multi-View Synthesizing for Metaverse [52.59476179535153]
  The metaverse is expected to provide immersive entertainment, education, and business applications. Virtual reality (VR) transmission over wireless networks is data- and computation-intensive. We have developed a novel multi-view synthesizing framework that can efficiently provide synthesizing, storage, and communication resources for wireless content delivery in the metaverse.
  arXiv  Detail & Related papers  (2023-12-18T13:51:56Z)
• 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)
• 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)
• DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder [73.1010640692609]
  We propose a VQ-VAE architecture model with a diffusion decoder (DiVAE) to work as the reconstructing component in image synthesis. Our model achieves state-of-the-art results and generates more photorealistic images specifically.
  arXiv  Detail & Related papers  (2022-06-01T10:39:12Z)

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.