  We propose a cyclic generative adversarial network with spatial-wise and
channel-wise attention modules for text-to-image synthesis. To accurately
depict and design scenes with multiple occluded objects, we design a
pre-trained ordering recovery model and a generative adversarial network to
predict layout and composite novel box lunch presentations. In the experiments,
we devise the Bento800 dataset to evaluate the performance of the text-to-image
synthesis model and the layout generation & image composition model. This paper
is the continuation of our previous paper works. We also present additional
experiments and qualitative performance comparisons to verify the effectiveness
of our proposed method. Bento800 dataset is available at
https://github.com/Yutong-Zhou-cv/Bento800_Dataset
