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In this paper, we address the task of semantic-guided scene generation. Oneopen challenge in scene generation is the difficulty of the generation of smallobjects and detailed local texture, which has been widely observed in globalimage-level generation methods. To tackle this issue, in this work we considerlearning the scene generation in a local context, and correspondingly design alocal class-specific generative network with semantic maps as a guidance, whichseparately constructs and learns sub-generators concentrating on the generationof different classes, and is able to provide more scene details. To learn morediscriminative class-specific feature representations for the local generation,a novel classification module is also proposed. To combine the advantage ofboth the global image-level and the local class-specific generation, a jointgeneration network is designed with an attention fusion module and adual-discriminator structure embedded. Extensive experiments on two scene imagegeneration tasks show superior generation performance of the proposed model.The state-of-the-art results are established by large margins on both tasks andon challenging public benchmarks. The source code and trained models areavailable at https://github.com/Ha0Tang/LGGAN.

Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation