  In this paper, we present an end-to-end approach to generate high-resolution
person images conditioned on texts only. State-of-the-art text-to-image
generation models are mainly designed for center-object generation, e.g.,
flowers and birds. Unlike center-placed objects with similar shapes and
orientation, person image generation is a more challenging task, for which we
observe the followings: 1) the generated images for the same person exhibit
visual details with identity-consistency, e.g., identity-related
textures/clothes/shoes across the images, and 2) those images should be
discriminant for being robust against the inter-person variations caused by
visual ambiguities. To address the above challenges, we develop an effective
generative model to produce person images with two novel mechanisms. In
particular, our first mechanism (called T-Person-GAN-ID) is to integrate the
one-stream generator with an identity-preserving network such that the
representations of generated data are regularized in their feature space to
ensure the identity-consistency. The second mechanism (called
T-Person-GAN-ID-MM) is based on the manifold mix-up to produce mixed images via
the linear interpolation across generated images from different manifold
identities, and we further enforce such interpolated images to be linearly
classified in the feature space. This amounts to learning a linear
classification boundary that can perfectly separate images from two identities.
Our proposed method is empirically validated to achieve a remarkable
improvement in text-to-person image generation. Our architecture is orthogonal
to StackGAN++ , and focuses on person image generation, with all of them
together to enrich the spectrum of GANs for the image generation task. Codes
are available on
\url{https://github.com/linwu-github/Person-Image-Generation.git}.

Computer Vision and Pattern Recognition