Regional Style and Color Transfer

• URL: http://arxiv.org/abs/2404.13880v4
• Date: Wed, 13 Nov 2024 18:31:18 GMT
• Title: Regional Style and Color Transfer
• Authors: Zhicheng Ding, Panfeng Li, Qikai Yang, Siyang Li, Qingtian Gong,
• Abstract summary: This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image.
• Score: 1.6993555918144923
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to image with foreground elements such as person figures. To address this limitation, we propose a new approach that leverages a segmentation network to precisely isolate foreground objects within the input image. Subsequently, style transfer is applied exclusively to the background region. The isolated foreground objects are then carefully reintegrated into the style-transferred background. To enhance the visual coherence between foreground and background, a color transfer step is employed on the foreground elements prior to their rein-corporation. Finally, we utilize feathering techniques to achieve a seamless amalgamation of foreground and background, resulting in a visually unified and aesthetically pleasing final composition. Extensive evaluations demonstrate that our proposed approach yields significantly more natural stylistic transformations compared to conventional methods.

Related papers

• Inversion-Free Style Transfer with Dual Rectified Flows [57.02757226679549]
  We propose a novel textitinversion-free style transfer framework based on dual rectified flows.<n>Our approach predicts content and style trajectories in parallel, then fuses them through a dynamic midpoint.<n>Experiments demonstrate generalization across diverse styles and content, providing an effective and efficient pipeline for style transfer.
  arXiv  Detail & Related papers  (2025-11-26T02:28:51Z)
• One-shot Embroidery Customization via Contrastive LoRA Modulation [20.463441212598273]
  We propose a novel contrastive learning framework that disentangles fine-grained style and content features with a single reference image.<n>To evaluate our method on fine-grained style transfer, we build a benchmark for embroidery customization.
  arXiv  Detail & Related papers  (2025-09-23T12:58:15Z)
• Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution [24.88532732093652]
  Style transfer presents a significant challenge, primarily centered on identifying an appropriate style representation. In contrast to existing approaches, we have discovered that latent features in vanilla diffusion models inherently contain natural style and content distributions. Our method adopts dual denoising paths to represent content and style references in latent space, subsequently guiding the content image denoising process with style latent codes.
  arXiv  Detail & Related papers  (2024-11-28T15:56:17Z)
• Style Transfer: From Stitching to Neural Networks [5.539031975261105]
  This article compares two style transfer methods in image processing. The traditional method synthesizes new images by stitching together small patches from existing images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background.
  arXiv  Detail & Related papers  (2024-09-01T04:07:03Z)
• TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization [59.412236435627094]
  TALE is a training-free framework harnessing the generative capabilities of text-to-image diffusion models. We equip TALE with two mechanisms dubbed Adaptive Latent Manipulation and Energy-guided Latent Optimization. Our experiments demonstrate that TALE surpasses prior baselines and attains state-of-the-art performance in image-guided composition.
  arXiv  Detail & Related papers  (2024-08-07T08:52:21Z)
• DiffStyler: Diffusion-based Localized Image Style Transfer [0.0]
  Image style transfer aims to imbue digital imagery with the distinctive attributes of style targets, such as colors, brushstrokes, shapes. Despite the advancements in arbitrary style transfer methods, a prevalent challenge remains the delicate equilibrium between content semantics and style attributes. This paper introduces DiffStyler, a novel approach that facilitates efficient and precise arbitrary image style transfer.
  arXiv  Detail & Related papers  (2024-03-27T11:19:34Z)
• Intrinsic Harmonization for Illumination-Aware Compositing [0.7366405857677227]
  We introduce a self-supervised illumination harmonization approach formulated in the intrinsic image domain. First, we estimate a simple global lighting model from mid-level vision representations to generate a rough shading for the foreground region. A network then refines this inferred shading to generate a re-shading that aligns with the background scene.
  arXiv  Detail & Related papers  (2023-12-06T18:59:03Z)
• Portrait Diffusion: Training-free Face Stylization with Chain-of-Painting [64.43760427752532]
  Face stylization refers to the transformation of a face into a specific portrait style. Current methods require the use of example-based adaptation approaches to fine-tune pre-trained generative models. This paper proposes a training-free face stylization framework, named Portrait Diffusion.
  arXiv  Detail & Related papers  (2023-12-03T06:48:35Z)
• Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate [58.83278629019384]
  Style transfer aims to render the style of a given image for style reference to another given image for content reference. Existing approaches either apply the holistic style of the style image in a global manner, or migrate local colors and textures of the style image to the content counterparts in a pre-defined way. We propose Any-to-Any Style Transfer, which enables users to interactively select styles of regions in the style image and apply them to the prescribed content regions.
  arXiv  Detail & Related papers  (2023-04-19T15:15:36Z)
• A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning [84.8813842101747]
  Unified Contrastive Arbitrary Style Transfer (UCAST) is a novel style representation learning and transfer framework. We present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components, i.e., a parallel contrastive learning scheme for style representation and style transfer, a domain enhancement module for effective learning of style distribution, and a generative network for style transfer.
  arXiv  Detail & Related papers  (2023-03-09T04:35:00Z)
• Image Harmonization by Matching Regional References [10.249228010611617]
  Recent image harmonization methods typically summarize the appearance pattern of global background and apply it to the global foreground without location discrepancy. For a real image, the appearances (illumination, color temperature, saturation, hue, texture, etc) of different regions can vary significantly. Previous methods, which transfer the appearance globally, are not optimal.
  arXiv  Detail & Related papers  (2022-04-10T16:23:06Z)
• UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation [0.0]
  We propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruction.
  arXiv  Detail & Related papers  (2021-08-13T08:06:29Z)
• SAFIN: Arbitrary Style Transfer With Self-Attentive Factorized Instance Normalization [71.85169368997738]
  Artistic style transfer aims to transfer the style characteristics of one image onto another image while retaining its content. Self-Attention-based approaches have tackled this issue with partial success but suffer from unwanted artifacts. This paper aims to combine the best of both worlds: self-attention and normalization.
  arXiv  Detail & Related papers  (2021-05-13T08:01:01Z)
• Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies [72.67344725725961]
  We present a novel unsupervised learning approach to image landmark discovery by incorporating the inter-subject landmark consistencies on facial images. This is achieved via an inter-subject mapping module that transforms original subject landmarks based on an auxiliary subject-related structure. To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images.
  arXiv  Detail & Related papers  (2020-04-16T20:38:16Z)

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.