Learning to see physics via visual de-animation

We introduce a paradigm for understanding physical scenes without human annotations. At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by physics and graphics engines. During training, the perception module and the generativ...

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Bibliographic Details
Main Authors: Wu, Jiajun (Author), Lu, Erika (Author), Kohli, Pushmeet (Author), Freeman, William T (Author), Tenenbaum, Joshua B (Author)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor)
Format: Article
Language:English
Published: Neural Information Processing Systems Foundation, Inc, 2021-02-09T21:08:02Z.
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