High-fidelity rendering of dynamic humans from monocular videos typically degrades catastrophically under occlusions. Existing solutions incorporate external priors-either hallucinating missing content via generative models, which induces severe temporal flickering, or imposing rigid geometric heuristics that fail to capture diverse appearances. To this end, we reformulate the task as a Maximum A Posteriori estimation problem under heteroscedastic observation noise. In this paper, we propose U-4DGS, a framework integrating a Probabilistic Deformation Network and a Joint Rasterization pipeline. This architecture renders pixel-aligned uncertainty maps that act as an adaptive gradient modulator, automatically attenuating artifacts from unreliable observations. Furthermore, to prevent geometric drift in regions lacking reliable visual cues, we enforce Confidence-Aware Regularizations, which leverage the learned uncertainty to selectively propagate spatial-temporal validity. Extensive experiments on the ZJU-MoCap and OcMotion datasets demonstrate that U-4DGS achieves state-of-the-art rendering fidelity and robustness.
DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction
Industrial Research Organisation, Canberra, ACT Australia；College of Engineering Computing \& Cybernetics, The Australian National University, Canberra, ACT Australia；Faculty of Engineering, The University of Hong Kong, Hong Kong SAR China；Industrial Research Organisation；College of Engineering Computing \& Cybernetics, The Australian National University；Faculty of Engineering, The University of Hong Kong
Learning Explicit Continuous Motion Representation for Dynamic Gaussian Splatting from Monocular Videos
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）
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）
M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Zhejiang University（浙江大学）；Beijing Institute of Technology（北京理工大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）
Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments
School of Information and Communication Engineering, Dalian University of Technology（信息与通信工程学院，大连理工大学）；School of Engineering Sciences, Saarland University of Applied Sciences（应用科学大学工程科学学院，萨尔兰应用科学大学）；Fraunhofer Institute for Nondestructive Testing（非破坏性检测研究所，弗劳恩霍夫）；College of Information Science and Technology, Beijing University of Chemical Technology（信息科学与技术学院，北京化工大学）；Faculty of Science and Engineering, Laval University（科学与工程学院，拉瓦尔大学）
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
The Chinese University of Hong Kong (CUHK)（香港中文大学）；University College London（伦敦大学学院）；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）；Shenzhen Research Institute, CUHK（香港中文大学深圳研究院）
GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；School of Artificial Intelligence and Data Science, University of Science and Technology of China（中国科学技术大学人工智能与数据科学学院）；The Chinese University of Hong Kong（香港中文大学）；Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；School of information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）
German Research Center for Artificial Intelligence – DFKI（德国人工智能研究中心–DFKI）；BMW Group（宝马集团）
Contrary to the ongoing trend in automotive applications towards usage of more diverse and more sensors, this work tries to solve the complex scene flow problem under a monocular camera setup, i.e. using a single sensor. Towards this end, we exploit the latest achievements in single image depth estimation, optical flow, and sparse-to-dense interpolation and propose a monocular combination approach (MonoComb) to compute dense scene flow. MonoComb uses optical flow to relate reconstructed 3D positions over time and interpolates occluded areas. This way, existing monocular methods are outperforme…
