Verbal-Person Nets: Pose-Guided Multi-Granularity Language-to-Person Generation

Person image generation conditioned on natural language allows us to personalize image editing in a user-friendly manner. This fashion, however, involves different granularities of semantic relevance between texts and visual content. Given a sentence describing an unknown person, we propose a novel pose-guided multi-granularity attention architecture to synthesize the person image in an end-to-end manner. To determine what content to draw at a global outline, the sentence-level description and pose feature maps are incorporated into a U-Net architecture to generate a coarse person image. To further enhance the fine-grained details, we propose to draw the human body parts with highly correlated textual nouns and determine the spatial positions with respect to target pose points. Our model is premised on a conditional generative adversarial network (GAN) that translates language description into a realistic person image. The proposed model is coupled with two-stream discriminators: 1) text-relevant local discriminators to improve the fine-grained appearance by identifying the region–text correspondences at the finer manipulation and 2) a global full-body discriminator to regulate the generation via a pose-weighting feature selection. Extensive experiments conducted on benchmarks validate the superiority of our method for person image generation.