ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement
While existing generation and unified models excel at general image generation, they struggle with tasks requiring deep reasoning, planning, and precise data-to-visual mapping abilities beyond general scenarios. To push beyond the existing limitations, we introduce a new and challenging task: creative table visualization, requiring the model to generate an infographic that faithfully and aesthetically visualizes the data from a given table. To address this challenge, we propose ShowTable, a pipeline that synergizes MLLMs with diffusion models via a progressive self-correcting process. The MLLM acts as the central orchestrator for reasoning the visual plan and judging visual errors to provide refined instructions, the diffusion execute the commands from MLLM, achieving high-fidelity results. To support this task and our pipeline, we introduce three automated data construction pipelines for training different modules. Furthermore, we introduce TableVisBench, a new benchmark with 800 challenging instances across 5 evaluation dimensions, to assess performance on this task. Experiments demonstrate that our pipeline, instantiated with different models, significantly outperforms baselines, highlighting its effective multi-modal reasoning, generation, and error correction capabilities.
69422e4f5d5b2dc1052748be
Zhihang Liu
claimed_verified
2025-12-17T10:08:08.977Z
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Zhihang Liu
lntzm
user
lntzm
69422e4f5d5b2dc1052748bf
Xiaoyi Bao
69422e4f5d5b2dc1052748c0
Pandeng Li
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Junjie Zhou
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Zhaohe Liao
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Yefei He
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Kaixun Jiang
69422e4f5d5b2dc1052748c5
Chen-Wei Xie
69422e4f5d5b2dc1052748c6
Yun Zheng
69422e4f5d5b2dc1052748c7
Hongtao Xie
ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement
While existing generation and unified models excel at general image generation, they struggle with tasks requiring deep reasoning, planning, and precise data-to-visual mapping abilities beyond general scenarios. To push beyond the existing limitations, we introduce a new and challenging task: creative table visualization, requiring the model to generate an infographic that faithfully and aesthetically visualizes the data from a given table. To address this challenge, we propose ShowTable, a pipeline that synergizes MLLMs with diffusion models via a progressive self-correcting process. The MLLM acts as the central orchestrator for reasoning the visual plan and judging visual errors to provide refined instructions, the diffusion execute the commands from MLLM, achieving high-fidelity results. To support this task and our pipeline, we introduce three automated data construction pipelines for training different modules. Furthermore, we introduce TableVisBench, a new benchmark with 800 challenging instances across 5 evaluation dimensions, to assess performance on this task. Experiments demonstrate that our pipeline, instantiated with different models, significantly outperforms baselines, highlighting its effective multi-modal reasoning, generation, and error correction capabilities.
ShowTable, a pipeline combining MLLMs and diffusion models, excels in creative table visualization by generating high-fidelity infographics from data tables, outperforming existing methods in multi-modal reasoning and error correction.
MLLMs diffusion models self-correcting process TableVisBench multi-modal reasoning generation error correction