Generative Adversarial Networks (GANs) can produce images of remarkable
complexity and realism but are generally structured to sample from a single
latent source ignoring the explicit spatial interaction between multiple
entities that could be present in a scene. Capturing such complex interactions
between different objects in the world, including their relative scaling,
spatial layout, occlusion, or viewpoint transformation is a challenging
problem. In this work, we propose a novel self-consistent
Composition-by-Decomposition (CoDe) network to compose a pair of objects. Given
object images from two distinct distributions, our model can generate a
realistic composite image from their joint distribution following the texture
and shape of the input objects. We evaluate our approach through qualitative
experiments and user evaluations. Our results indicate that the learned model
captures potential interactions between the two object domains, and generates
realistic composed scenes at test time.

本文提出 Composition-by-Decomposition 网络，采用两个独立分布的物体生成具有真实纹理和形状的合成图像，以此捕捉多物体之间的空间相互作用关系，从而在生成场景方面获得更好的效果。