6 papers
Shape of Motion: 4D Reconstruction from a Single Video
Qianqian Wang, Vickie Ye, Hang Gao +4
Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches depend on templates, are effe…
Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos
Linyi Jin, Richard Tucker, Zhengqi Li +3
Learning to understand dynamic 3D scenes from imagery is crucial for applications ranging from robotics to scene reconstruction. Yet, unlike other problems where large-scale superv…
Can Generative Video Models Help Pose Estimation?
Ruojin Cai, Jason Y. Zhang, Philipp Henzler +3
Pairwise pose estimation from images with little or no overlap is an open challenge in computer vision. Existing methods, even those trained on large-scale datasets, struggle in th…
MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videos
Zhengqi Li, Richard Tucker, Forrester Cole +6
We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes. Most conventional stru…
Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusion
Boyang Deng, Richard Tucker, Zhengqi Li +3
We present a method for generating Streetscapes-long sequences of views through an on-the-fly synthesized city-scale scene. Our generation is conditioned by language input (e.g., c…
Generative Image Dynamics
Zhengqi Li, Richard Tucker, Noah Snavely +1
We present an approach to modeling an image-space prior on scene motion. Our prior is learned from a collection of motion trajectories extracted from real video sequences depicting…