65 citations · 476 across the 71 of their papers we have counts for
6 papers · 1 filter
SparseFusion: Distilling View-conditioned Diffusion for 3D Reconstruction
Zhizhuo Zhou, Shubham Tulsiani
We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically…
Monocular Dynamic View Synthesis: A Reality Check
Hang Gao, Ruilong Li, Shubham Tulsiani +2
We study the recent progress on dynamic view synthesis (DVS) from monocular video. Though existing approaches have demonstrated impressive results, we show a discrepancy between th…
RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild
Jason Y. Zhang, Deva Ramanan, Shubham Tulsiani
We describe a data-driven method for inferring the camera viewpoints given multiple images of an arbitrary object. This task is a core component of classic geometric pipelines such…
What's in your hands? 3D Reconstruction of Generic Objects in Hands
Yufei Ye, Abhinav Gupta, Shubham Tulsiani
Our work aims to reconstruct hand-held objects given a single RGB image. In contrast to prior works that typically assume known 3D templates and reduce the problem to 3D pose estim…
Pre-train, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction
Kalyan Vasudev Alwala, Abhinav Gupta, Shubham Tulsiani
Our work learns a unified model for single-view 3D reconstruction of objects from hundreds of semantic categories. As a scalable alternative to direct 3D supervision, our work reli…
AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation
Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh +1
Powerful priors allow us to perform inference with insufficient information. In this paper, we propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as sh…