  In this paper, we propose a novel variational generator framework for
conditional GANs to catch semantic details for improving the generation quality
and diversity. Traditional generators in conditional GANs simply concatenate
the conditional vector with the noise as the input representation, which is
directly employed for upsampling operations. However, the hidden condition
information is not fully exploited, especially when the input is a class label.
Therefore, we introduce a variational inference into the generator to infer the
posterior of latent variable only from the conditional input, which helps
achieve a variable augmented representation for image generation. Qualitative
and quantitative experimental results show that the proposed method outperforms
the state-of-the-art approaches and achieves the realistic controllable images.
