510 citations · 1.9k across the 80 of their papers we have counts for
11 papers · 2 filters
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…
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…
Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features
Yuanbo Xiangli, Ruojin Cai, Hanyu Chen +2
Accurate 3D reconstruction is frequently hindered by visual aliasing, where visually similar but distinct surfaces (aka, doppelgangers), are incorrectly matched. These spurious mat…
KFC-W: Generating 3D-Consistent Videos from Unposed Internet Photos
Gene Chou, Kai Zhang, Sai Bi +5
We address the problem of generating videos from unposed internet photos. A handful of input images serve as keyframes, and our model interpolates between them to simulate a path m…
LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias
Haian Jin, Hanwen Jiang, Hao Tan +6
We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce two a…
ObjectCarver: Semi-automatic segmentation, reconstruction and separation of 3D objects
Gemmechu Hassena, Jonathan Moon, Ryan Fujii +4
Implicit neural fields have made remarkable progress in reconstructing 3D surfaces from multiple images; however, they encounter challenges when it comes to separating individual o…
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…
MegaScenes: Scene-Level View Synthesis at Scale
Joseph Tung, Gene Chou, Ruojin Cai +5
Scene-level novel view synthesis (NVS) is fundamental to many vision and graphics applications. Recently, pose-conditioned diffusion models have led to significant progress by extr…
Neural Gaffer: Relighting Any Object via Diffusion
Haian Jin, Yuan Li, Fujun Luan +6
Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting. Many prior methods either support only…
PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation
Tianyuan Zhang, Hong-Xing Yu, Rundi Wu +5
Realistic object interactions are crucial for creating immersive virtual experiences, yet synthesizing realistic 3D object dynamics in response to novel interactions remains a sign…