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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation, Inc
2021
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| Online Access: | https://hdl.handle.net/1721.1/129728 |