Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles

• URL: http://arxiv.org/abs/2506.06846v1
• Date: Sat, 07 Jun 2025 15:54:34 GMT
• Title: Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles
• Authors: Yangkai Lin, Jiabao Lei, Kui jia,
• Abstract summary: 3D Gaussian Splatting(GS) has emerged as a promising and efficient method for realistic 3D scene modeling.<n>We introduce a novel 3D GS stylization solution termed Multi-StyleGS to tackle these challenges.
• Score: 45.648346391757336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there has been a growing demand to stylize a given 3D scene to align with the artistic style of reference images for creative purposes. While 3D Gaussian Splatting(GS) has emerged as a promising and efficient method for realistic 3D scene modeling, there remains a challenge in adapting it to stylize 3D GS to match with multiple styles through automatic local style transfer or manual designation, while maintaining memory efficiency for stylization training. In this paper, we introduce a novel 3D GS stylization solution termed Multi-StyleGS to tackle these challenges. In particular, we employ a bipartite matching mechanism to au tomatically identify correspondences between the style images and the local regions of the rendered images. To facilitate local style transfer, we introduce a novel semantic style loss function that employs a segmentation network to apply distinct styles to various objects of the scene and propose a local-global feature matching to enhance the multi-view consistency. Furthermore, this technique can achieve memory efficient training, more texture details and better color match. To better assign a robust semantic label to each Gaussian, we propose several techniques to regularize the segmentation network. As demonstrated by our comprehensive experiments, our approach outperforms existing ones in producing plausible stylization results and offering flexible editing.

Related papers

• Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles [10.472018360278085]
  Current state-of-the-art 3D stylization methods typically involve computationally intensive test-time optimization to transfer artistic features into a pretrained representation.<n>We demonstrate a novel approach to achieve direct 3D stylization in less than a second using unposed sparse-view scene images and an arbitrary style image.
  arXiv  Detail & Related papers  (2025-05-27T11:47:15Z)
• FPGS: Feed-Forward Semantic-aware Photorealistic Style Transfer of Large-Scale Gaussian Splatting [19.307856423583356]
  FPGS is a feed-forward photorealistic style transfer method of large-scale radiance fields represented by Gaussian Splatting.<n>FPGS stylizes large-scale 3D scenes with arbitrary, multiple style reference images without additional optimization.<n>In experiments, we demonstrate that FPGS achieves favorable photorealistic quality scene stylization for large-scale static and dynamic 3D scenes.
  arXiv  Detail & Related papers  (2025-03-11T23:52:56Z)
• G3DST: Generalizing 3D Style Transfer with Neural Radiance Fields across Scenes and Styles [45.92812062685523]
  Existing methods for 3D style transfer need extensive per-scene optimization for single or multiple styles. In this work, we overcome the limitations of existing methods by rendering stylized novel views from a NeRF without the need for per-scene or per-style optimization. Our findings demonstrate that this approach achieves a good visual quality comparable to that of per-scene methods.
  arXiv  Detail & Related papers  (2024-08-24T08:04:19Z)
• StyleSplat: 3D Object Style Transfer with Gaussian Splatting [0.3374875022248866]
  Style transfer can enhance 3D assets with diverse artistic styles, transforming creative expression. We introduce StyleSplat, a method for stylizing 3D objects in scenes represented by 3D Gaussians from reference style images. We demonstrate its effectiveness across various 3D scenes and styles, showcasing enhanced control and customization in 3D creation.
  arXiv  Detail & Related papers  (2024-07-12T17:55:08Z)
• StylizedGS: Controllable Stylization for 3D Gaussian Splatting [53.0225128090909]
  StylizedGS is an efficient 3D neural style transfer framework with adaptable control over perceptual factors. Our method achieves high-quality stylization results characterized by faithful brushstrokes and geometric consistency with flexible controls.
  arXiv  Detail & Related papers  (2024-04-08T06:32:11Z)
• Gaussian Splatting in Style [32.41970914897462]
  3D sceneization extends the work of neural style transfer to 3D. A vital challenge in this problem is to maintain the uniformity of the stylized appearance across multiple views. We propose a novel architecture trained on a collection of style images that, at test time, produces real time high-quality stylized novel views.
  arXiv  Detail & Related papers  (2024-03-13T13:06:31Z)
• StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields [52.19291190355375]
  StyleRF (Style Radiance Fields) is an innovative 3D style transfer technique. It employs an explicit grid of high-level features to represent 3D scenes, with which high-fidelity geometry can be reliably restored via volume rendering. It transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer.
  arXiv  Detail & Related papers  (2023-03-19T08:26:06Z)
• 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)
• 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.