ACDG-VTON: Accurate and Contained Diffusion Generation for Virtual Try-On

• URL: http://arxiv.org/abs/2403.13951v1
• Date: Wed, 20 Mar 2024 19:45:06 GMT
• Title: ACDG-VTON: Accurate and Contained Diffusion Generation for Virtual Try-On
• Authors: Jeffrey Zhang, Kedan Li, Shao-Yu Chang, David Forsyth,
• Abstract summary: We propose a training scheme that limits the scope in which diffusion is trained. Our method runs multi-garment try-on in a single inference cycle and can support high-quality zoomed-in generations without training in higher resolutions.
• Score: 6.228968332207618
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Virtual Try-on (VTON) involves generating images of a person wearing selected garments. Diffusion-based methods, in particular, can create high-quality images, but they struggle to maintain the identities of the input garments. We identified this problem stems from the specifics in the training formulation for diffusion. To address this, we propose a unique training scheme that limits the scope in which diffusion is trained. We use a control image that perfectly aligns with the target image during training. In turn, this accurately preserves garment details during inference. We demonstrate our method not only effectively conserves garment details but also allows for layering, styling, and shoe try-on. Our method runs multi-garment try-on in a single inference cycle and can support high-quality zoomed-in generations without training in higher resolutions. Finally, we show our method surpasses prior methods in accuracy and quality.

Related papers

• EfficientVITON: An Efficient Virtual Try-On Model using Optimized Diffusion Process [2.0451307225357427]
  Core challenge lies in realistic image-to-image translation, where clothing must fit diverse human forms, poses, and figures. Early methods, which used 2D transformations, offered speed, but image quality was often disappointing and lacked the nuance of deep learning. Recent advances in diffusion models have shown promise for high-fidelity translation, yet the current crop of virtual try-on tools still struggle with detail loss and warping issues. This paper proposes EfficientVITON, a new virtual try-on system leveraging the impressive pre-trained Stable Diffusion model.
  arXiv  Detail & Related papers  (2025-01-20T22:44:53Z)
• Learning Flow Fields in Attention for Controllable Person Image Generation [59.10843756343987]
  Controllable person image generation aims to generate a person image conditioned on reference images. We propose learning flow fields in attention (Leffa), which explicitly guides the target query to attend to the correct reference key. Leffa achieves state-of-the-art performance in controlling appearance (virtual try-on) and pose (pose transfer), significantly reducing fine-grained detail distortion.
  arXiv  Detail & Related papers  (2024-12-11T15:51:14Z)
• FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on [73.13242624924814]
  Garment perception enhancement technique, FitDiT, is designed for high-fidelity virtual try-on using Diffusion Transformers (DiT) We introduce a garment texture extractor that incorporates garment priors evolution to fine-tune garment feature, facilitating to better capture rich details such as stripes, patterns, and text. We also employ a dilated-relaxed mask strategy that adapts to the correct length of garments, preventing the generation of garments that fill the entire mask area during cross-category try-on.
  arXiv  Detail & Related papers  (2024-11-15T11:02:23Z)
• 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)
• Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder [57.574544285878794]
  Ada-Adapter is a novel framework for few-shot style personalization of diffusion models. Our method enables efficient zero-shot style transfer utilizing a single reference image. We demonstrate the effectiveness of our approach on various artistic styles, including flat art, 3D rendering, and logo design.
  arXiv  Detail & Related papers  (2024-07-08T02:00:17Z)
• TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On [34.51850518458418]
  Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. We propose an effective and efficient framework, termed TryOn-Adapter.
  arXiv  Detail & Related papers  (2024-04-01T03:15:41Z)
• 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)
• OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on [7.46772222515689]
  OOTDiffusion is a novel network architecture for realistic and controllable image-based virtual try-on. We leverage the power of pretrained latent diffusion models, designing an outfitting UNet to learn the garment detail features. Our experiments on the VITON-HD and Dress Code datasets demonstrate that OOTDiffusion efficiently generates high-quality try-on results.
  arXiv  Detail & Related papers  (2024-03-04T07:17:44Z)
• 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)
• 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)
• Dressing in the Wild by Watching Dance Videos [69.7692630502019]
  This paper attends to virtual try-on in real-world scenes and brings improvements in authenticity and naturalness. We propose a novel generative network called wFlow that can effectively push up garment transfer to in-the-wild context.
  arXiv  Detail & Related papers  (2022-03-29T08:05:45Z)

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.