6 papers
World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
Liyuan Zhu, Shengyu Huang, Amrita Mazumdar +6
We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos. Our approach conditions a video model on dense, p…
Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis
Zhengfei Kuang, Adam Sun, Liyuan Zhu +7
Modern vehicle platforms are equipped with a rich sensor suite, including LiDAR, calibrated multi-camera rigs, and accurate ego-motion, that in principle offers strong signal for r…
WildPose: A Unified Framework for Robust Pose Estimation in the Wild
Jianhao Zheng, Liyuan Zhu, Zihan Zhu +1
Estimating camera pose in dynamic environments is a critical challenge, as most visual SLAM and SfM methods assume static scenes. While recent dynamic-aware methods exist, they are…
GaussFusion: Improving 3D Reconstruction in the Wild with A Geometry-Informed Video Generator
Liyuan Zhu, Manjunath Narayana, Michal Stary +3
We present GaussFusion, a novel approach for improving 3D Gaussian splatting (3DGS) reconstructions in the wild through geometry-informed video generation. GaussFusion mitigates co…
Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching
Yue Pan, Tao Sun, Liyuan Zhu +4
Point cloud registration aligns multiple unposed point clouds into a common reference frame and is a core step for 3D reconstruction and robot localization without initial guess. I…
Rectified Point Flow: Generic Point Cloud Pose Estimation
Tao Sun, Liyuan Zhu, Shengyu Huang +2
We introduce Rectified Point Flow, a unified parameterization that formulates pairwise point cloud registration and multi-part shape assembly as a single conditional generative pro…