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In this paper, we propose an end-end model for producing furniture layout forinterior scene synthesis from the random vector. This proposed model is aimedto support professional interior designers to produce the interior decorationsolutions more quickly. The proposed model combines a conditional floor-planmodule of the room, a conditional graphical floor-plan module of the room and aconditional layout module. As compared with the prior work on scene synthesis,our proposed three modules enhance the ability of auto-layout generation giventhe dimensional category of the room. We conduct our experiments on theproposed real-world interior layout dataset that contains $191208$ designs fromthe professional designers. Our numerical results demonstrate that the proposedmodel yields higher-quality layouts in comparison with the state-of-the-artmodel. The dataset and code are released\href{https://github.com/CODE-SUBMIT/dataset3}{Dataset,Code}

End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph