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Semantic Bottleneck Scene Generation
[article]
2019
arXiv
pre-print
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes. We assume pixel-wise segmentation labels are available during training and use them to learn the scene structure. During inference, our model first synthesizes a realistic segmentation layout from scratch, then synthesizes a realistic scene conditioned on
arXiv:1911.11357v1
fatcat:id7o6lwt6bejfcs2tlnxt2rrb4