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