PosterMate: Audience-driven Collaborative Persona Agents for Poster Design
This addresses the problem of integrating diverse audience feedback in design processes for designers, though it is incremental in applying existing AI models to a specific domain.
The paper tackles the challenge of gathering and reconciling diverse audience feedback for poster design by introducing PosterMate, a system that uses AI-driven persona agents to simulate audience perspectives and facilitate collaborative feedback, with user studies showing it captures overlooked viewpoints and synthesizes perspectives effectively.
Poster designing can benefit from synchronous feedback from target audiences. However, gathering audiences with diverse perspectives and reconciling them on design edits can be challenging. Recent generative AI models present opportunities to simulate human-like interactions, but it is unclear how they may be used for feedback processes in design. We introduce PosterMate, a poster design assistant that facilitates collaboration by creating audience-driven persona agents constructed from marketing documents. PosterMate gathers feedback from each persona agent regarding poster components, and stimulates discussion with the help of a moderator to reach a conclusion. These agreed-upon edits can then be directly integrated into the poster design. Through our user study (N=12), we identified the potential of PosterMate to capture overlooked viewpoints, while serving as an effective prototyping tool. Additionally, our controlled online evaluation (N=100) revealed that the feedback from an individual persona agent is appropriate given its persona identity, and the discussion effectively synthesizes the different persona agents' perspectives.