  Developing deep generative models has been an emerging field due to the
ability to model and generate complex data for various purposes, such as image
synthesis and molecular design. However, the advancement of deep generative
models is limited by challenges to generate objects that possess multiple
desired properties: 1) the existence of complex correlation among real-world
properties is common but hard to identify; 2) controlling individual property
enforces an implicit partially control of its correlated properties, which is
difficult to model; 3) controlling multiple properties under various manners
simultaneously is hard and under-explored. We address these challenges by
proposing a novel deep generative framework that recovers semantics and the
correlation of properties through disentangled latent vectors. The correlation
is handled via an explainable mask pooling layer, and properties are precisely
retained by generated objects via the mutual dependence between latent vectors
and properties. Our generative model preserves properties of interest while
handling correlation and conflicts of properties under a multi-objective
optimization framework. The experiments demonstrate our model's superior
performance in generating data with desired properties.
