Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers

Academic poster generation is a crucial yet challenging task in scientific communication, requiring the compression of long-context interleaved documents into a single, visually coherent page. To address this challenge, we introduce Paper2Poster, the first benchmark and metric suite for poster generation, which pairs recent conference papers with author-designed posters and evaluates outputs on (i) Visual Quality--semantic alignment with human posters, (ii) Textual Coherence--language fluency, (iii) Holistic Assessment--six fine-grained aesthetic and informational criteria scored by a VLM-as-judge, and notably (iv) PaperQuiz--the poster's ability to convey core paper content as measured by VLMs answering generated quizzes. Building on this benchmark, we propose PosterAgent, a top down, visual in the loop multi agent pipeline: the (a) Parser distills the paper into a structured asset library; the (b) Planner aligns text-visual pairs into a binary tree layout that preserves reading order and spatial balance; and the (c) Painter-Commenter loop refines each panel by executing rendering code and using VLM feedback to eliminate overflow and ensure alignment.In our comprehensive evaluation, we find that GPT 4o outputs--though visually appealing at first glance--often exhibit noisy text and poor PaperQuiz scores; We find that reader engagement is the primary aesthetic bottleneck, as human designed posters rely largely on visual semantics to convey meaning.Our fully open source Paper2Poster pipeline outperforms GPT 4o-based systems across nearly all metrics while consuming 87 \% fewer tokens. These findings chart clear directions for the next generation of fully automated poster generation models.