ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908913645912064 |
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| author | Liu, Zhihang Bao, Xiaoyi Li, Pandeng Zhou, Junjie Liao, Zhaohe He, Yefei Jiang, Kaixun Xie, Chen-Wei Zheng, Yun Xie, Hongtao |
| author_facet | Liu, Zhihang Bao, Xiaoyi Li, Pandeng Zhou, Junjie Liao, Zhaohe He, Yefei Jiang, Kaixun Xie, Chen-Wei Zheng, Yun Xie, Hongtao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13303 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement Liu, Zhihang Bao, Xiaoyi Li, Pandeng Zhou, Junjie Liao, Zhaohe He, Yefei Jiang, Kaixun Xie, Chen-Wei Zheng, Yun Xie, Hongtao Computer Vision and Pattern Recognition 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. |
| title | ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.13303 |