We propose to investigate detecting and characterizing the 3D planar articulation of objects from ordinary videos. While seemingly easy for humans, this problem poses many challenges for computers. We propose to approach this problem by combining a top-down detection system that finds planes that can be articulated along with an optimization approach that solves for a 3D plane that can explain a sequence of observed articulations. We show that this system can be trained on a combination of videos and 3D scan datasets. When tested on a dataset of challenging Internet videos and the Charades dataset, our approach obtains strong performance. Project site: https://jasonqsy.github.io/Articulation3D
@article{arxiv.2203.16531,
title = {Understanding 3D Object Articulation in Internet Videos},
author = {Shengyi Qian and Linyi Jin and Chris Rockwell and Siyi Chen and David F. Fouhey},
journal= {arXiv preprint arXiv:2203.16531},
year = {2022}
}