Novel view synthesis from limited observations remains an important and persistent task. However, high efficiency in existing NeRF-based few-shot view synthesis is often compromised to obtain an accurate 3D representation. To address this challenge, we propose a few-shot view synthesis framework based on 3D Gaussian Splatting that enables real-time and photo-realistic view synthesis with as few as three training views. The proposed method, dubbed FSGS, handles the extremely sparse initialized SfM points with a thoughtfully designed Gaussian Unpooling process. Our method iteratively distributes new Gaussians around the most representative locations, subsequently infilling local details in vacant areas. We also integrate a large-scale pre-trained monocular depth estimator within the Gaussians optimization process, leveraging online augmented views to guide the geometric optimization towards an optimal solution. Starting from sparse points observed from limited input viewpoints, our FSGS can accurately grow into unseen regions, comprehensively covering the scene and boosting the rendering quality of novel views. Overall, FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets, including LLFF, Mip-NeRF360, and Blender. Project website: https://zehaozhu.github.io/FSGS/.
Opacity-Gradient Driven Density Control for Compact and Efficient Few-Shot 3D Gaussian Splatting
Department of Computer Science and Physics, Wilfrid Laurier University（计算机科学与物理系，威尔弗里德·劳里尔大学）；Department of Computer Science（计算机科学系）；Physics, Wilfrid Laurier University（物理系，威尔弗里德·劳里尔大学）
Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis
QUT Centre for Robotics, Australia（昆士兰大学机器人中心，澳大利亚）；ARIAM Hub, Australia（ARIAM中心，澳大利亚）；University of Wuppertal, Germany（乌尔姆大学，德国）；University of Osnabrück, Germany（奥斯纳布吕克大学，德国）
Geometry-Consistent 4D Gaussian Splatting for Sparse-Input Dynamic View Synthesis
The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学）；Southern University of Science and Technology, Shenzhen, China（南方科技大学）；Indian Institute of Technology, Jodhpur, India（印度理工学院，朱道尔）；Institute of Cyberspace Technology, HKCT Institute of Higher Education, Hong Kong, China（网络空间技术研究所，香港高等教育学院）
Differentiable image sampling in the form of backward warping has seen broad adoption in tasks like depth estimation and optical flow prediction. In contrast, how to perform forward warping has seen less attention, partly due to additional challenges such as resolving the conflict of mapping multiple pixels to the same target location in a differentiable way. We propose softmax splatting to address this paradigm shift and show its effectiveness on the application of frame interpolation. Specifically, given two input frames, we forward-warp the frames and their feature pyramid representations b…
SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior
ETH Zürich（苏黎世联邦理工学院）；Baidu Research（百度研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；Tianjin University（天津大学）
GaussianEmoTalker: Real-Time Emotional Talking Head Synthesis with Audio-Driven and Blendshape-Based 3D Gaussian Splatting
PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院，教育部高维信息智能感知与系统重点实验室，PCA实验室）；Tsientang Institute for Advanced Study（浙江大学前沿交叉研究院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting
Beihang University（北京航空航天大学）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；Griffith University（格里菲斯大学）；RIKEN AIP（理化学研究所人工智能项目）；The University of Tokyo（东京大学）
