We present an approach to synthesizing photographic images conditioned on
semantic layouts. Given a semantic label map, our approach produces an image
with photographic appearance that conforms to the input layout. The approach
thus functions as a rendering engine that takes a two-dimensional semantic
specification of the scene and produces a corresponding photographic image.
Unlike recent and contemporaneous work, our approach does not rely on
adversarial training. We show that photographic images can be synthesized from
semantic layouts by a single feedforward network with appropriate structure,
trained end-to-end with a direct regression objective. The presented approach
scales seamlessly to high resolutions; we demonstrate this by synthesizing
photographic images at 2-megapixel resolution, the full resolution of our
training data. Extensive perceptual experiments on datasets of outdoor and
indoor scenes demonstrate that images synthesized by the presented approach are
considerably more realistic than alternative approaches. The results are shown
in the supplementary video at this https URL

该研究证明使用带有适当结构的前馈网络进行直接回归，而不必依赖对抗性训练，可以从语义布局中合成具有照片外貌的图像，并通过实验证明此方法比其他方法更加逼真。