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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

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

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

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

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

Controllable 3D style transfer seeks to restyle a 3D asset so that its textures match a reference image while preserving the integrity and multi-view consistency. The prevalent methods either rely on direct reference style token injection…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Yuteng Ye , Zheng Zhang , Qinchuan Zhang , Di Wang , Youjia Zhang , Wenxiao Zhang , Wei Yang , Yuan Liu

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

We present InstantStyleGaussian, an innovative 3D style transfer method based on the 3D Gaussian Splatting (3DGS) scene representation. By inputting a target-style image, it quickly generates new 3D GS scenes. Our method operates on…

Computer Vision and Pattern Recognition · Computer Science 2024-08-27 Xin-Yi Yu , Jun-Xin Yu , Li-Bo Zhou , Yan Wei , Lin-Lin Ou

3D style transfer refers to the artistic stylization of 3D assets based on reference style images. Recently, 3DGS-based stylization methods have drawn considerable attention, primarily due to their markedly enhanced training and rendering…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Yian Zhao , Rushi Ye , Ruochong Zheng , Zesen Cheng , Chaoran Feng , Jiashu Yang , Pengchong Qiao , Chang Liu , Jie Chen

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

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…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Liyuan Zhu , Shengqu Cai , Shengyu Huang , Gordon Wetzstein , Naji Khosravan , Iro Armeni

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

Applying style transfer to a full 3D environment is a challenging task that has seen many developments since the advent of neural rendering. 3D Gaussian splatting (3DGS) has recently pushed further many limits of neural rendering in terms…

Computer Vision and Pattern Recognition · Computer Science 2025-04-11 Bruno Galerne , Jianling Wang , Lara Raad , Jean-Michel Morel

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

The stylization of 3D scenes is an increasingly attractive topic in 3D vision. Although image style transfer has been extensively researched with promising results, directly applying 2D style transfer methods to 3D scenes often fails to…

Computer Vision and Pattern Recognition · Computer Science 2024-11-18 Yushen Zuo , Jun Xiao , Kin-Chung Chan , Rongkang Dong , Cuixin Yang , Zongqi He , Hao Xie , Kin-Man Lam

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

Current 3D Gaussian Splatting stylization approaches are limited in their ability to represent diverse artistic styles, frequently defaulting to low-level texture replacement or yielding semantically inconsistent outputs. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2026-02-06 Cailin Zhuang , Yaoqi Hu , Xuanyang Zhang , Wei Cheng , Jiacheng Bao , Shengqi Liu , Yiying Yang , Xianfang Zeng , Gang Yu , Ming Li

Transferring 2D textures onto complex 3D scenes plays a vital role in enhancing the efficiency and controllability of 3D multimedia content creation. However, existing 3D style transfer methods primarily focus on transferring abstract…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Wenjie Liu , Zhongliang Liu , Junwei Shu , Changbo Wang , Yang Li

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

Novel-view synthesis aims to generate novel views of a scene from multiple input images or videos, and recent advancements like 3D Gaussian splatting (3DGS) have achieved notable success in producing photorealistic renderings with efficient…

Computer Vision and Pattern Recognition · Computer Science 2024-10-22 Xi Liu , Chaoyi Zhou , Siyu Huang
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