5 papers · 1 filter
ZipMap: Linear-Time Stateful 3D Reconstruction via Test-Time Training
Haian Jin, Rundi Wu, Tianyuan Zhang +4
Feed-forward transformer models have driven rapid progress in 3D vision, but state-of-the-art methods such as VGGT and have a computational cost that scales quadratically wit…
Neural USD: An object-centric framework for iterative editing and control
Alejandro Escontrela, Shrinu Kushagra, Sjoerd van Steenkiste +5
Amazing progress has been made in controllable generative modeling, especially over the last few years. However, some challenges remain. One of them is precise and iterative object…
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…
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…
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…