Poster
in
Workshop: NeurIPS 2023 Workshop on Machine Learning for Creativity and Design
Personalized Comic Story Generation
WENXUAN PENG · Peter Schaldenbrand · Jean Oh
Sat 16 Dec 6:15 a.m. PST — 2:40 p.m. PST
We introduce PCSG, a diffusion-based text-to-image synthesis framework for supporting comic story generation, a domain in which authors require control over the consistency, composition, and diversity of content. To support these three requirements, PCSG has controllable plugins for (1) character consistency, (2) scene layout specification, and (3) character pose specification. The novel combination of these plugins enables users to exert fine-grained control and manifest their envisioned comic narratives with personalized characters. Our system provides flexibility which greatly improved user satisfaction in our study over existing approaches such as using MidJourney or Stable Diffusion. To further advance this field and facilitate community engagement, we will open source our code soon.