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Related papers: Stylos: Multi-View 3D Stylization with Single-Forw…

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As XR technology continues to advance rapidly, 3D generation and editing are increasingly crucial. Among these, stylization plays a key role in enhancing the appearance of 3D models. By utilizing stylization, users can achieve consistent…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Dingxi Zhang , Yu-Jie Yuan , Zhuoxun Chen , Fang-Lue Zhang , Zhenliang He , Shiguang Shan , Lin Gao

We introduce StyleGaussian, a novel 3D style transfer technique that allows instant transfer of any image's style to a 3D scene at 10 frames per second (fps). Leveraging 3D Gaussian Splatting (3DGS), StyleGaussian achieves style transfer…

Computer Vision and Pattern Recognition · Computer Science 2024-03-13 Kunhao Liu , Fangneng Zhan , Muyu Xu , Christian Theobalt , Ling Shao , Shijian Lu

Conventional 3D style transfer methods rely on a fixed reference image to apply artistic patterns to 3D scenes. However, in practical applications such as virtual or augmented reality, users often prefer more flexible inputs, including…

Computer Vision and Pattern Recognition · Computer Science 2026-04-07 Xingyu Miao , Xueqi Qiu , Haoran Duan , Yawen Huang , Xian Wu , Jingjing Deng , Yang Long

3D scene stylization 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. A vast majority of the previous works achieve this by…

Computer Vision and Pattern Recognition · Computer Science 2024-09-09 Abhishek Saroha , Mariia Gladkova , Cecilia Curreli , Dominik Muhle , Tarun Yenamandra , Daniel Cremers

3D neural style transfer has gained significant attention for its potential to provide user-friendly stylization with spatial consistency. However, existing 3D style transfer methods often fall short in terms of inference efficiency,…

Computer Vision and Pattern Recognition · Computer Science 2024-10-15 Wanlin Liang , Hongbin Xu , Weitao Chen , Feng Xiao , Wenxiong Kang

The success of 3DGS in generative and editing applications has sparked growing interest in 3DGS-based style transfer. However, current methods still face two major challenges: (1) multi-view inconsistency often leads to style conflicts,…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Yitong Yang , Yinglin Wang , Changshuo Wang , Huajie Wang , Shuting He

We introduce G-Style, a novel algorithm designed to transfer the style of an image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting is a powerful 3D representation for novel view synthesis, as -- compared to other…

Graphics · Computer Science 2024-09-06 Áron Samuel Kovács , Pedro Hermosilla , Renata G. Raidou

Recent advances in text-driven 3D scene editing and stylization, which leverage the powerful capabilities of 2D generative models, have demonstrated promising outcomes. However, challenges remain in ensuring high-quality stylization and…

Graphics · Computer Science 2026-03-03 Haruo Fujiwara , Yusuke Mukuta , Tatsuya Harada

Exploring real-world spaces using novel-view synthesis is fun, and reimagining those worlds in a different style adds another layer of excitement. Stylized worlds can also be used for downstream tasks where there is limited training data…

Computer Vision and Pattern Recognition · Computer Science 2025-03-19 Jamie Wynn , Zawar Qureshi , Jakub Powierza , Jamie Watson , Mohamed Sayed

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…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Yangkai Lin , Jiabao Lei , Kui jia

Gaussian Splatting (GS) has recently emerged as an efficient representation for rendering 3D scenes from 2D images and has been extended to images, videos, and dynamic 4D content. However, applying style transfer to GS-based…

Computer Vision and Pattern Recognition · Computer Science 2025-10-27 Kornel Howil , Joanna Waczyńska , Piotr Borycki , Tadeusz Dziarmaga , Marcin Mazur , Przemysław Spurek

In this work, we focus on zero-shot 3D style transfer that can generate multi-view consistent stylized views of the 3D scene given an arbitrary style image. We primarily tackle the issue of data scarcity in 3D style transfer, which arises…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Xin Dong , Yunzhi Teng , Wenfeng Deng , Yansong Tang

Stylizing a dynamic scene based on an exemplar image is critical for various real-world applications, including gaming, filmmaking, and augmented and virtual reality. However, achieving consistent stylization across both spatial and…

Computer Vision and Pattern Recognition · Computer Science 2025-01-08 Abhishek Saroha , Florian Hofherr , Mariia Gladkova , Cecilia Curreli , Or Litany , Daniel Cremers

We present Style3D, a novel approach for generating stylized 3D objects from a content image and a style image. Unlike most previous methods that require case- or style-specific training, Style3D supports instant 3D object stylization. Our…

Computer Vision and Pattern Recognition · Computer Science 2024-12-05 Bingjie Song , Xin Huang , Ruting Xie , Xue Wang , Qing Wang

Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2024-07-15 Sahil Jain , Avik Kuthiala , Prabhdeep Singh Sethi , Prakanshul Saxena

Visual content creation has spurred a soaring interest given its applications in mobile photography and AR / VR. Style transfer and single-image 3D photography as two representative tasks have so far evolved independently. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2021-12-07 Fangzhou Mu , Jian Wang , Yicheng Wu , Yin Li

Recent advancements in neural representations, such as Neural Radiance Fields and 3D Gaussian Splatting, have increased interest in applying style transfer to 3D scenes. While existing methods can transfer style patterns onto 3D-consistent…

Computer Vision and Pattern Recognition · Computer Science 2025-09-05 Jimin Xu , Bosheng Qin , Tao Jin , Zhou Zhao , Zhenhui Ye , Jun Yu , Fei Wu

The growing demand for rapid and scalable 3D asset creation has driven interest in feed-forward 3D reconstruction methods, with 3D Gaussian Splatting (3DGS) emerging as an effective scene representation. While recent approaches have…

Computer Vision and Pattern Recognition · Computer Science 2026-02-05 Joanna Kaleta , Bartosz Świrta , Kacper Kania , Przemysław Spurek , Marek Kowalski

3D style transfer enables the creation of visually expressive 3D content, enriching the visual appearance of 3D scenes and objects. However, existing VGG- and CLIP-based methods struggle to model multi-view consistency within the model…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Yitong Yang , Xuexin Liu , Yinglin Wang , Jing Wang , Hao Dou , Changshuo Wang , Shuting He

Stylizing 3D scenes instantly while maintaining multi-view consistency and faithfully resembling a style image remains a significant challenge. Current state-of-the-art 3D stylization methods typically involve computationally intensive…

Computer Vision and Pattern Recognition · Computer Science 2025-10-21 Peng Wang , Xiang Liu , Peidong Liu
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