Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convo-lutional networks and generative adversarial nets. The benefits of ou...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Neural Information Processing Systems Foundation
2017
|
| Online Access: | http://hdl.handle.net/1721.1/112753 https://orcid.org/0000-0002-4176-343X https://orcid.org/0000-0001-5031-6618 https://orcid.org/0000-0002-2231-7995 https://orcid.org/0000-0002-1925-2035 |