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Jeremy Reizenstein

4 accepted papers

2023

Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories

CVPR 2023highlight

Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors. Earlier works on sparse rigid object reconstruction successfully learned such priors from large datasets such as CO3D. In this paper, we…

2021

Common Objects in 3D: Large-Scale Learning and Evaluation of Real-Life 3D Category Reconstruction

ICCV 2021poster

Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data. Our main goal is to facilitate advances in this field by collecting real-world data in a magnitude simila…

Cited by 491PDFcodeScholar
2021

Unsupervised Learning of 3D Object Categories From Videos in the Wild

CVPR 2021poster

Recently, numerous works have attempted to learn 3D reconstructors of textured 3D models of visual categories given a training set of annotated static images of objects. In this paper, we seek to decrease the amount of needed supervision by leveraging a collection of object-centric videos captured i…

Cited by 81PDFScholar
2019

PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments

NeurIPS 2019poster

Given a set of a reference RGBD views of an indoor environment, and a new viewpoint, our goal is to predict the view from that location. Prior work on new-view generation has predominantly focused on significantly constrained scenarios, typically involving artificially rendered views of isolated CAD…

Cited by 24SourcePDFScholar