title: Paper2Poster: Towards Multimodal Poster Automation from Scientific
    Papers
summary: 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
  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, and 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 variants (e.g. based on the
  Qwen-2.5 series) outperform existing 4o-driven multi-agent systems across
  nearly all metrics, while using 87% fewer tokens. It transforms a 22-page paper
  into a finalized yet editable .pptx poster - all for just $0.005. These
  findings chart clear directions for the next generation of fully automated
  poster-generation models. The code and datasets are available at
  https://github.com/Paper2Poster/Paper2Poster.
paper_authors._id: 68366e5a2ae719660434bb5a
paper_authors.name: Wei Pang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:18.507Z
paper_authors._id: 65164444bc0631719873af81
paper_authors.avatarUrl: /avatars/dab8b90db8bbd00806268fe276e3ea36.svg
paper_authors.fullname: Wei Pang
paper_authors.type: user
paper_authors.user: weipang142857
paper_authors._id: 68366e5a2ae719660434bb5b
paper_authors.name: Kevin Qinghong Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T10:11:26.491Z
paper_authors._id: 64440be5af034cdfd69ca3a7
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paper_authors.fullname: Qinghong (Kevin) Lin
paper_authors.type: user
paper_authors.user: KevinQHLin
paper_authors._id: 68366e5a2ae719660434bb5c
paper_authors.name: Xiangru Jian
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:16.627Z
paper_authors._id: 636865b8cca0a0a962c21f3f
paper_authors.avatarUrl: /avatars/ed0b5eb84ba91afa263c1069db25d909.svg
paper_authors.fullname: Xiangru (Edward) Jian
paper_authors.type: user
paper_authors.user: HideOnBush
paper_authors._id: 68366e5a2ae719660434bb5d
paper_authors.name: Xi He
paper_authors._id: 68366e5a2ae719660434bb5e
paper_authors.name: Philip Torr
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:13:28.232Z
paper_authors._id: 6565ed28a5ec0231cb07225f
paper_authors.avatarUrl: /avatars/7f95bba9aa7811d56eecb380827abfac.svg
paper_authors.fullname: prof philip torr
paper_authors.type: user
paper_authors.user: philiptorr
paper_title: Paper2Poster: Towards Multimodal Poster Automation from Scientific
    Papers
paper_summary: 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
  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, and 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 variants (e.g. based on the
  Qwen-2.5 series) outperform existing 4o-driven multi-agent systems across
  nearly all metrics, while using 87% fewer tokens. It transforms a 22-page paper
  into a finalized yet editable .pptx poster - all for just $0.005. These
  findings chart clear directions for the next generation of fully automated
  poster-generation models. The code and datasets are available at
  https://github.com/Paper2Poster/Paper2Poster.
paper_ai_summary: A benchmark and metric suite for poster generation evaluates visual quality, coherence, and content accuracy, leading to a multi-agent pipeline that outperforms existing models with reduced computational cost.
paper_ai_keywords: top-down pipeline multi-agent pipeline VLM-as-judge binary-tree layout rendering code VLM feedback parser planner painter-commenter loop GPT-4 Qwen-2.5 automated poster-generation models