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
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.
68366e5a2ae719660434bb5a
Wei Pang
claimed_verified
2025-05-28T08:58:18.507Z
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Wei Pang
user
weipang142857
68366e5a2ae719660434bb5b
Kevin Qinghong Lin
claimed_verified
2025-05-28T10:11:26.491Z
64440be5af034cdfd69ca3a7
https://cdn-avatars.huggingface.co/v1/production/uploads/64440be5af034cdfd69ca3a7/qmx24QiDFT29vleCxL9TX.jpeg
Qinghong (Kevin) Lin
user
KevinQHLin
68366e5a2ae719660434bb5c
Xiangru Jian
claimed_verified
2025-05-28T08:58:16.627Z
636865b8cca0a0a962c21f3f
/avatars/ed0b5eb84ba91afa263c1069db25d909.svg
Xiangru (Edward) Jian
user
HideOnBush
68366e5a2ae719660434bb5d
Xi He
68366e5a2ae719660434bb5e
Philip Torr
admin_assigned
2025-05-28T12:13:28.232Z
6565ed28a5ec0231cb07225f
/avatars/7f95bba9aa7811d56eecb380827abfac.svg
prof philip torr
user
philiptorr
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
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.
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.
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