ShowTable：以协作反思与精炼解锁创意表格可视化新境界

摘要

尽管现有生成模型与统一模型在通用图像生成方面表现出色，但在需要超越常规场景的深度推理、规划能力及精确数据到视觉映射的任务中仍存在不足。为突破现有局限，我们提出一项新颖且具有挑战性的任务：创意表格可视化，要求模型根据给定表格数据生成兼具信息忠实度与视觉美学的信息图。针对这一挑战，我们提出ShowTable框架，通过渐进式自我修正过程实现多模态大语言模型与扩散模型的协同工作。该框架以MLLM作为核心协调器，负责视觉方案推理与视觉误差判定以提供优化指令，扩散模型则执行MLLM的指令以实现高保真度生成。为支持该任务及框架，我们开发了三套自动化数据构建流程用于训练不同模块。此外，我们推出TableVisBench新基准数据集，包含800个涵盖5个评估维度的挑战性实例，用于系统评估任务性能。实验表明，基于不同模型实例化的我们的框架显著超越基线方法，凸显了其有效的多模态推理、生成及纠错能力。

English

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：以协作反思与精炼解锁创意表格可视化新境界

ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement

摘要

Support