ReStyle3D: Scene-Level Appearance Transfer with Semantic Correspondences

• URL: http://arxiv.org/abs/2502.10377v1
• Date: Fri, 14 Feb 2025 18:54:21 GMT
• Title: ReStyle3D: Scene-Level Appearance Transfer with Semantic Correspondences
• Authors: Liyuan Zhu, Shengqu Cai, Shengyu Huang, Gordon Wetzstein, Naji Khosravan, Iro Armeni,
• Abstract summary: ReStyle3D is a framework for scene-level appearance transfer from a single style image to a real-world scene represented by multiple views. It combines explicit semantic correspondences with multi-view consistency to achieve precise and coherent stylization. Our code, pretrained models, and dataset will be publicly released to support new applications in interior design, virtual staging, and 3D-consistent stylization.
• Score: 33.06053818091165
• License:
• Abstract: We introduce ReStyle3D, a novel framework for scene-level appearance transfer from a single style image to a real-world scene represented by multiple views. The method combines explicit semantic correspondences with multi-view consistency to achieve precise and coherent stylization. Unlike conventional stylization methods that apply a reference style globally, ReStyle3D uses open-vocabulary segmentation to establish dense, instance-level correspondences between the style and real-world images. This ensures that each object is stylized with semantically matched textures. It first transfers the style to a single view using a training-free semantic-attention mechanism in a diffusion model. It then lifts the stylization to additional views via a learned warp-and-refine network guided by monocular depth and pixel-wise correspondences. Experiments show that ReStyle3D consistently outperforms prior methods in structure preservation, perceptual style similarity, and multi-view coherence. User studies further validate its ability to produce photo-realistic, semantically faithful results. Our code, pretrained models, and dataset will be publicly released, to support new applications in interior design, virtual staging, and 3D-consistent stylization.

Related papers

• Style3D: Attention-guided Multi-view Style Transfer for 3D Object Generation [9.212876623996475]
  Style3D is a novel approach for generating stylized 3D objects from a content image and a style image. By establishing an interplay between structural and stylistic features across multiple views, our approach enables a holistic 3D stylization process.
  arXiv  Detail & Related papers  (2024-12-04T18:59:38Z)
• MRStyle: A Unified Framework for Color Style Transfer with Multi-Modality Reference [32.64957647390327]
  We introduce MRStyle, a framework that enables color style transfer using multi-modality reference, including image and text. For text reference, we align the text feature of stable diffusion priors with the style feature of our IRStyle to perform text-guided color style transfer (TRStyle) Our TRStyle method is highly efficient in both training and inference, producing notable open-set text-guided transfer results.
  arXiv  Detail & Related papers  (2024-09-09T00:01:48Z)
• InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation [4.1177497612346]
  Style transfer is an inventive process designed to create an image that maintains the essence of the original while embracing the visual style of another. We introduce InstantStyle-Plus, an approach that prioritizes the integrity of the original content while seamlessly integrating the target style.
  arXiv  Detail & Related papers  (2024-06-30T18:05:33Z)
• ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model [73.95608242322949]
  Stylized Text-to-Image Generation (STIG) aims to generate images from text prompts and style reference images. We present ArtWeaver, a novel framework that leverages pretrained Stable Diffusion to address challenges such as misinterpreted styles and inconsistent semantics.
  arXiv  Detail & Related papers  (2024-05-24T07:19:40Z)
• CLIP3Dstyler: Language Guided 3D Arbitrary Neural Style Transfer [41.388313754081544]
  We propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler) Compared with the previous 2D method CLIPStyler, we are able to stylize a 3D scene and generalize to novel scenes without re-train our model. We conduct extensive experiments to show the effectiveness of our model on text-guided 3D scene style transfer.
  arXiv  Detail & Related papers  (2023-05-25T05:30:13Z)
• 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)
• DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization [66.42741426640633]
  DiffStyler is a dual diffusion processing architecture to control the balance between the content and style of diffused results. We propose a content image-based learnable noise on which the reverse denoising process is based, enabling the stylization results to better preserve the structure information of the content image.
  arXiv  Detail & Related papers  (2022-11-19T12:30:44Z)
• Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning [84.8813842101747]
  Contrastive Arbitrary Style Transfer (CAST) is a new style representation learning and style transfer method via contrastive learning. Our framework consists of three key components, i.e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
  arXiv  Detail & Related papers  (2022-05-19T13:11:24Z)
• Unified Implicit Neural Stylization [80.59831861186227]
  This work explores a new intriguing direction: training a stylized implicit representation. We conduct a pilot study on a variety of implicit functions, including 2D coordinate-based representation, neural radiance field, and signed distance function. Our solution is a Unified Implicit Neural Stylization framework, dubbed INS.
  arXiv  Detail & Related papers  (2022-04-05T02:37:39Z)
• STALP: Style Transfer with Auxiliary Limited Pairing [36.23393954839379]
  We present an approach to example-based stylization of images that uses a single pair of a source image and its stylized counterpart. We demonstrate how to train an image translation network that can perform real-time semantically meaningful style transfer to a set of target images.
  arXiv  Detail & Related papers  (2021-10-20T11:38:41Z)
• 3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer [66.48720190245616]
  We propose a learning-based approach for style transfer between 3D objects. The proposed method can synthesize new 3D shapes both in the form of point clouds and meshes. We extend our technique to implicitly learn the multimodal style distribution of the chosen domains.
  arXiv  Detail & Related papers  (2020-11-26T16:59:12Z)

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.