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Related papers: Style4D-Bench: A Benchmark Suite for 4D Stylizatio…

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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

Text-to-4D generation is rapidly developing and widely applied in various scenarios. However, existing methods often fail to incorporate adequate spatio-temporal modeling and prompt alignment within a unified framework, resulting in…

Computer Vision and Pattern Recognition · Computer Science 2025-04-28 Yunze Deng , Haijun Xiong , Bin Feng , Xinggang Wang , Wenyu Liu

Instruction-guided generative models, especially those using text-to-image (T2I) and text-to-video (T2V) diffusion frameworks, have advanced the field of content editing in recent years. To extend these capabilities to 4D scene, we…

Computer Vision and Pattern Recognition · Computer Science 2025-04-02 Hasan Iqbal , Nazmul Karim , Umar Khalid , Azib Farooq , Zichun Zhong , Chen Chen , Jing Hua

Recent advancements in dynamic 3D scene reconstruction have shown promising results, enabling high-fidelity 3D novel view synthesis with improved temporal consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an appealing…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Seungjun Oh , Younggeun Lee , Hyejin Jeon , Eunbyung Park

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

Recent advancements in foundation models for 2D vision have substantially improved the analysis of dynamic scenes from monocular videos. However, despite their strong generalization capabilities, these models often lack 3D consistency, a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Haoran Zhou , Gim Hee Lee

Modeling dynamic 3D scenes is challenging due to their high-dimensional nature, which requires aggregating information from multiple views to reconstruct time-evolving 3D geometry and motion. We present a novel multi-video 4D Gaussian…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Yonghan Lee , Tsung-Wei Huang , Shiv Gehlot , Jaehoon Choi , Guan-Ming Su , Dinesh Manocha

We introduce the Stress-Guided Lightweight Design Benchmark (SGLDBench), a comprehensive benchmark suite for applying and evaluating material layout strategies to generate stiff, lightweight designs in 3D domains. SGLDBench provides a…

Computational Engineering, Finance, and Science · Computer Science 2025-06-03 Junpeng Wang , Dennis R. Bukenberger , Simon Niedermayr , Christoph Neuhauser , Jun Wu , Rüdiger Westermann

3D Gaussian Splatting (3DGS) serves as a highly performant and efficient encoding of scene geometry, appearance, and semantics. Moreover, grounding language in 3D scenes has proven to be an effective strategy for 3D scene understanding.…

Computer Vision and Pattern Recognition · Computer Science 2026-01-27 Mengjiao Ma , Qi Ma , Yue Li , Jiahuan Cheng , Runyi Yang , Bin Ren , Nikola Popovic , Mingqiang Wei , Nicu Sebe , Luc Van Gool , Theo Gevers , Martin R. Oswald , Danda Pani Paudel

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

World Generation Models are emerging as a cornerstone of next-generation multimodal intelligence systems. Unlike traditional 2D visual generation, World Models aim to construct realistic, dynamic, and physically consistent 3D/4D worlds from…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yiting Lu , Wei Luo , Peiyan Tu , Haoran Li , Hanxin Zhu , Zihao Yu , Xingrui Wang , Xinyi Chen , Xinge Peng , Xin Li , Zhibo Chen

Urban scene reconstruction requires modeling both static infrastructure and dynamic elements while supporting diverse environmental conditions. We present \textbf{StyledStreets}, a multi-style street simulator that achieves…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Yuyin Chen , Yida Wang , Xueyang Zhang , Kun Zhan , Peng Jia , Yifei Zhan , Xianpeng Lang

3D stylization is central to game development, virtual reality, and digital arts, where the demand for diverse assets calls for scalable methods that support fast, high-fidelity manipulation. Existing text-to-3D stylization methods…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Melis Ocal , Xiaoyan Xing , Yue Li , Ngo Anh Vien , Sezer Karaoglu , Theo Gevers

We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Tianqi Liu , Zihao Huang , Zhaoxi Chen , Guangcong Wang , Shoukang Hu , Liao Shen , Huiqiang Sun , Zhiguo Cao , Wei Li , Ziwei Liu

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

Recently, Gaussian splatting has received more and more attention in the field of static scene rendering. Due to the low computational overhead and inherent flexibility of explicit representations, plane-based explicit methods are popular…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Jiawei Xu , Zexin Fan , Jian Yang , Jin Xie

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

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

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

3D Gaussian Splatting (3DGS) has garnered significant attention due to its superior scene representation fidelity and real-time rendering performance, especially for dynamic 3D scene reconstruction (\textit{i.e.}, 4D reconstruction).…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Henan Wang , Hanxin Zhu , Xinliang Gong , Tianyu He , Xin Li , Zhibo Chen
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