  In this paper, we investigate the problem of text-to-pedestrian synthesis,
which has many potential applications in art, design, and video surveillance.
Existing methods for text-to-bird/flower synthesis are still far from solving
this fine-grained image generation problem, due to the complex structure and
heterogeneous appearance that the pedestrians naturally take on. To this end,
we propose the Multi-Grained Discrimination enhanced Generative Adversarial
Network, that capitalizes a human-part-based Discriminator (HPD) and a
self-cross-attended (SCA) global Discriminator in order to capture the
coherence of the complex body structure. A fined-grained word-level attention
mechanism is employed in the HPD module to enforce diversified appearance and
vivid details. In addition, two pedestrian generation metrics, named Pose Score
and Pose Variance, are devised to evaluate the generation quality and
diversity, respectively. We conduct extensive experiments and ablation studies
on the caption-annotated pedestrian dataset, CUHK Person Description Dataset.
The substantial improvement over the various metrics demonstrates the efficacy
of MGD-GAN on the text-to-pedestrian synthesis scenario.
