Weakly Supervised High-Fidelity Clothing Model Generation

• URL: http://arxiv.org/abs/2112.07200v1
• Date: Tue, 14 Dec 2021 07:15:15 GMT
• Title: Weakly Supervised High-Fidelity Clothing Model Generation
• Authors: Ruili Feng, Cheng Ma, Chengji Shen, Xin Gao, Zhenjiang Liu, Xiaobo Li, Kairi Ou and Zhengjun Zha
• Abstract summary: We propose a cheap yet scalable weakly-supervised method called Deep Generative Projection (DGP) to address this specific scenario. We show that projecting the rough alignment of clothing and body onto the StyleGAN space can yield photo-realistic wearing results.
• Score: 67.32235668920192
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales. However, the expensive proprietary model images challenge the existing image virtual try-on methods in this scenario, as most of them need to be trained on considerable amounts of model images accompanied with paired clothes images. In this paper, we propose a cheap yet scalable weakly-supervised method called Deep Generative Projection (DGP) to address this specific scenario. Lying in the heart of the proposed method is to imitate the process of human predicting the wearing effect, which is an unsupervised imagination based on life experience rather than computation rules learned from supervisions. Here a pretrained StyleGAN is used to capture the practical experience of wearing. Experiments show that projecting the rough alignment of clothing and body onto the StyleGAN space can yield photo-realistic wearing results. Experiments on real scene proprietary model images demonstrate the superiority of DGP over several state-of-the-art supervised methods when generating clothing model images.

Related papers

• 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)
• AniDress: Animatable Loose-Dressed Avatar from Sparse Views Using Garment Rigging Model [58.035758145894846]
  We introduce AniDress, a novel method for generating animatable human avatars in loose clothes using very sparse multi-view videos. A pose-driven deformable neural radiance field conditioned on both body and garment motions is introduced, providing explicit control of both parts. Our method is able to render natural garment dynamics that deviate highly from the body and well to generalize to both unseen views and poses.
  arXiv  Detail & Related papers  (2024-01-27T08:48:18Z)
• A Two-stage Personalized Virtual Try-on Framework with Shape Control and Texture Guidance [7.302929117437442]
  This paper proposes a brand new personalized virtual try-on model (PE-VITON), which uses the two stages (shape control and texture guidance) to decouple the clothing attributes. The proposed model can effectively solve the problems of weak reduction of clothing folds, poor generation effect under complex human posture, blurred edges of clothing, and unclear texture styles in traditional try-on methods.
  arXiv  Detail & Related papers  (2023-12-24T13:32:55Z)
• 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)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• Implementing and Experimenting with Diffusion Models for Text-to-Image Generation [0.0]
  Two models, DALL-E 2 and Imagen, have demonstrated that highly photorealistic images could be generated from a simple textual description of an image. Text-to-image models require exceptionally large amounts of computational resources to train, as well as handling huge datasets collected from the internet. This thesis contributes by reviewing the different approaches and techniques used by these models, and then by proposing our own implementation of a text-to-image model.
  arXiv  Detail & Related papers  (2022-09-22T12:03:33Z)
• Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing [49.96406805006839]
  We introduce pose-driven avatars with explicit modeling of clothing that exhibit both realistic clothing dynamics and photorealistic appearance learned from real-world data. Our key contribution is a physically-inspired appearance network, capable of generating photorealistic appearance with view-dependent and dynamic shadowing effects even for unseen body-clothing configurations.
  arXiv  Detail & Related papers  (2022-06-30T17:58:20Z)
• Style and Pose Control for Image Synthesis of Humans from a Single Monocular View [78.6284090004218]
  StylePoseGAN is a non-controllable generator to accept conditioning of pose and appearance separately. Our network can be trained in a fully supervised way with human images to disentangle pose, appearance and body parts. StylePoseGAN achieves state-of-the-art image generation fidelity on common perceptual metrics.
  arXiv  Detail & Related papers  (2021-02-22T18:50:47Z)

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.