  Story visualization advances the traditional text-to-image generation by
enabling multiple image generation based on a complete story. This task
requires machines to 1) understand long text inputs and 2) produce a globally
consistent image sequence that illustrates the contents of the story. A key
challenge of consistent story visualization is to preserve characters that are
essential in stories. To tackle the challenge, we propose to adapt a recent
work that augments Vector-Quantized Variational Autoencoders (VQ-VAE) with a
text-tovisual-token (transformer) architecture. Specifically, we modify the
text-to-visual-token module with a two-stage framework: 1) character token
planning model that predicts the visual tokens for characters only; 2) visual
token completion model that generates the remaining visual token sequence,
which is sent to VQ-VAE for finalizing image generations. To encourage
characters to appear in the images, we further train the two-stage framework
with a character-token alignment objective. Extensive experiments and
evaluations demonstrate that the proposed method excels at preserving
characters and can produce higher quality image sequences compared with the
strong baselines. Codes can be found in https://github.com/sairin1202/VP-CSV
