In this paper, we address the task of semantic-guided scene generation. One
open challenge in scene generation is the difficulty of the generation of small
objects and detailed local texture, which has been widely observed in global
image-level generation methods. To tackle this issue, in this work we consider
learning the scene generation in a local context, and correspondingly design a
local class-specific generative network with semantic maps as a guidance, which
separately constructs and learns sub-generators concentrating on the generation
of different classes, and is able to provide more scene details. To learn more
discriminative class-specific feature representations for the local generation,
a novel classification module is also proposed. To combine the advantage of
both the global image-level and the local class-specific generation, a joint
generation network is designed with an attention fusion module and a
dual-discriminator structure embedded. Extensive experiments on two scene image
generation tasks show superior generation performance of the proposed model.
The state-of-the-art results are established by large margins on both tasks and
on challenging public benchmarks. The source code and trained models are
available at this https URL

本文提出了一种语义引导场景生成的方法，设计了一个局部类特定的生成网络，配合语义地图和注意力融合模块实现了全局和局部场景细节生成的结合，进而在两个场景图像生成任务上取得了最新的最佳结果。