Explicit 3D scene representations such as 3D Gaussian Splatting (3DGS) have recently enabled high-quality, real-time novel view synthesis. However, these methods assume static scenes, limiting realism in the presence of dynamic phenomena ubiquitous in real-world environments. While coherent motion (e.g., humans or vehicles) can be captured and reconstructed, high-frequency effects such as flowing water remain intractable under casual capture conditions, where dense multi-view, high-speed acquisition is not feasible. We present a method to animate water in static 3DGS scenes, enabling visually plausible motion without requiring dynamic capture or physical simulation. Our approach estimates 2D optical flow from reference images or model renderings and lifts it into a consistent 3D Eulerian motion field. We propose a cinemagraph-inspired algorithm that applies this motion field to deform Gaussian primitives, producing temporally seamless looping animations. The resulting representation retains real-time rendering performance, significantly enhances perceptual realism, and preserves free-viewpoint navigation within the scene. We evaluate our method on a newly captured dataset of in-the-wild waterfall scenes and demonstrate clear improvements over prior work in dynamic Gaussian splatting and 3D cinemagraph generation in visual quality and computational efficiency.
4D-GSW: Kinematic-Aware Spatio-Temporal Consistent Watermarking for 4D Gaussian Splatting
Southeast University（东南大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy（广东人工智能与数字经济实验室）；University of Chinese Academy of Sciences（中国科学院大学）
X-SG$^2$S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
South China University of Technology（南方科技大学）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；Peking University（北京大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室）；Harbin Institute of Technology（哈尔滨工业大学）
BitC-3DGS: High-Capacity 3D Gaussian Splatting Watermarking via Bit Compression
School of Cyber Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李科金医学院）；College of Electronic Engineer, National University of Defense and Technology（国防科技大学电子工程学院）；Institute of AI for Industries, Chinese Academy of Sciences（中国科学院人工智能产业研究所）；School of Computer Science and Engineering, Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University（东南大学计算机科学与工程学院，教育部新一代人工智能技术及其交叉应用重点实验室）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Faculty of Science and Technology, UOW College Hong Kong（UOW学院香港科技学院理学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程学院）
GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Computing and Information Technology, Great Bay University（大湾区大学计算与信息技术学院）；Pazhou Lab (Huangpu)（琶洲实验室（黄埔））
AquaFlow: A Monocular Gaussian Splatting SLAM for Underwater Streaming Reconstruction
Zhejiang University（浙江大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）
RainyGS: Efficient Rain Synthesis with Physically-Based Gaussian Splatting
Peking University（北京大学）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；School of Computer Science, Peking University（北京大学计算机学院）；School of EECS, Peking University（北京大学电子工程与计算机科学学院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究院）；State Key Laboratory of Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）
In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as flowing water and billowing smoke. Our method relies on the observation that this type of natural motion can be convincingly reproduced from a static Eulerian motion description, i.e. a single, temporally constant flow field that defines the immediate motion of a particle at a given 2D location. We use an image-to-image translation network to encode motion priors of natural scenes collected from online videos, so tha…
I-X and Department of Electrical and Electronic Engineering, Imperial College London, UK（I-X 和 电气与电子工程系，帝国理工学院伦敦分校）；Frontier Robotics, The National Robotarium, Edinburgh UK（前沿机器人技术，国家机器人中心，爱丁堡）
EndoMD-SLAM: Endoscopic Gaussian Splatting SLAM under Optical Degradation with Memory and Static-Transient Decomposition
Texas A&M University（德克萨斯农工大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Minnesota（明尼苏达大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction
School of Astronautics Beihang University Beijing China（航天学院 北航 北京 中国）；School of Artificial Intelligence Beihang University Beijing China（人工智能学院 北航 北京 中国）；Zhongguancun Academy Beijing China（中关村学院 北京 中国）；Beihang University School of Astronautics Beijing China（北航 航天学院 北京 中国）；Beihang University（北航）；Zhongguancun Academy（中关村学院）
BALTIC: A Benchmark and Cross-Domain Strategy for 3D Reconstruction Across Air and Underwater Domains Under Varying Illumination
School of Engineering & Physical Sciences, Heriot-Watt University（赫瑞瓦德大学工程与物理科学学院）；Marine Data Science Group, Christian Albrechts University of Kiel（基尔大学海洋数据科学组）；Frontier Robotics, The National Robotarium（前沿机器人，国家机器人中心）
RAGA: Real Time Ray Traced Gaussian Shadow Casting for 3DGS Avatar-Scene Interaction
Tübingen AI Center, University of Tübingen, Germany（图宾根人工智能中心，图宾根大学，德国）；Max Planck Institute for Informatics, Saarland Informatics Campus, Germany（马克斯·普朗克研究所信息学院，萨尔兰信息学院，德国）；Imperial College London, UK（伦敦帝国理工学院，英国）；King Abdullah University of Science and Technology (KAUST), Saudi Arabia（国王阿卜杜勒阿齐兹科技大学（KAUST），沙特阿拉伯）；Snap Inc., USA（Snap公司，美国）
University of Tsukuba（筑波大学）；Toyohashi University of Technology（丰桥技术科学大学）
Automatic generation of a high-quality video from a single image remains a challenging task despite the recent advances in deep generative models. This paper proposes a method that can create a high-resolution, long-term animation using convolutional neural networks (CNNs) from a single landscape image where we mainly focus on skies and waters. Our key observation is that the motion (e.g., moving clouds) and appearance (e.g., time-varying colors in the sky) in natural scenes have different time scales. We thus learn them separately and predict them with decoupled control while handling future …
Characterizing Detectability in 3DGS Poisoning: A Stage-wise Benchmark
Temasek Laboratories, Singapore University of Technology and Design（新加坡科技与设计大学Temasek实验室）；Vietnam National University, Ho Chi Minh City（越南国家大学胡志明市分校）；University of Information Technology, VNU-HCM（越南国家大学胡志明市信息技术大学）；Agency for Science, Technology, and Research (A*STAR)（科技研究局（A*STAR））
TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian
Huazhong University of Science and Technology（华中科技大学）；Zhongguancun Academy（中关村学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Zhengzhou University（郑州大学）；Hainan University（海南大学）；Shenzhen University（深圳大学）
