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  <meta name="description" content="What You Think Is What You Get: A novel framework for intuitive transfer function design in volume visualization using multimodal large language models. This research bridges the gap between user intent and transfer function design through natural language and visual inputs.">
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  <meta name="description" content="IntuiTF: A novel framework for intuitive transfer function design in volume visualization using multimodal large language models. This research bridges the gap between user intent and transfer function design through natural language and visual inputs.">
  <meta property="og:title" content="IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering"/>
  <meta property="og:description" content="A novel framework for intuitive transfer function design in volume visualization using multimodal large language models, enabling both novices and experts to efficiently explore and customize volume visualizations."/>
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            <h1 class="title is-1 publication-title">IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering</h1>
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                <a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Yiyao Wang</a><sup>1</sup>,</span>
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                <a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Bo Pan</a><sup>1</sup>,</span>
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                <a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Ke Wang</a><sup>1</sup>,</span>
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                <a href="FOURTH AUTHOR PERSONAL LINK" target="_blank">Han Liu</a><sup>1</sup>,</span>
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                <a href="FIFTH AUTHOR PERSONAL LINK" target="_blank">Jinyuan Mao</a><sup>1</sup>,</span>
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                <a href="SIXTH AUTHOR PERSONAL LINK" target="_blank">Yuxin Liu</a><sup>1</sup>,</span>
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                <a href="SEVENTH AUTHOR PERSONAL LINK" target="_blank">Minfeng Zhu</a><sup>2</sup>,</span>
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                <a href="NINTH AUTHOR PERSONAL LINK" target="_blank">Weifeng Chen</a><sup>3</sup>,</span>
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                  <a href="EIGHTH AUTHOR PERSONAL LINK" target="_blank">Bo Zhang</a><sup>2</sup>,</span>
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                <a href="ELEVENTH AUTHOR PERSONAL LINK" target="_blank">Wei Chen</a><sup>1</sup>
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                <span class="author-block"><sup>1</sup>China State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China</span><br>
                <span class="author-block"><sup>2</sup>Zhejiang University, Hangzhou, China</span><br>
                <span class="author-block"><sup>3</sup>Zhejiang University of Finance and Economics, Hangzhou, China</span>

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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Direct volume rendering (DVR) is a fundamental technique for visualizing volumetric data, where transfer functions (TFs) play a crucial role in extracting meaningful structures. However, designing effective TFs remains unintuitive due to the semantic gap between user intent and TF parameter space. Although numerous TF optimization methods have been proposed to mitigate this issue, existing approaches still face two major challenges: the vast exploration space and limited generalizability. To address these issues, we propose IntuiTF, a novel framework that leverages Multimodal Large Language Models (MLLMs) to guide TF optimization in alignment with user intent. Specifically, our method consists of two key components: \ding{172} an evolution-driven explorer for effective exploration of the TF space, and \ding{173} an MLLM-guided human-aligned evaluator that provides generalizable visual feedback on rendering quality. The explorer and the evaluator together establish an efficient Trial-Insight-Replanning paradigm for TF space exploration. We further extend our framework with an interactive TF design system. We demonstrate the broad applicability of our framework through three case studies and validate the effectiveness of each component through extensive experiments.
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            Our WYTWYG framework presents an TF optimization approach that effectively bridges user intent with transfer function design. Our methodology consists of two key components: (A) a transfer function optimization approach that combines (A<sub>1</sub>) an evolution-based explorer following the Trial-Insight-Replanning optimization formulation to iteratively generate and refine transfer functions with adaptive replanning capabilities, and (A<sub>2</sub>) an MLLM-based evaluator that assesses visualizations according to both formal quality metrics and user intent alignment. (B) Our interactive transfer function design system implements a three-stage interaction workflow of initialization-customization-refinement, enabling users to express their visualization intentions through natural text descriptions or reference images while progressively refining results to achieve desired outcomes.
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      <h2 class="title is-3">Results</h2>

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      <h3 class="title is-4">Text-guided Results</h3>
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        <p>
          Examples of transfer functions designed using our approach. The first row illustrates text-guided designs, with the corresponding user descriptions shown below each image.
          The second row illustrates image-guided designs, where each bottom inset shows the user-provided reference.
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              We demonstrate the practical applicability of our IntuiTF framework through comprehensive use cases that showcase how users can effectively leverage multimodal inputs to design transfer functions for various volume visualization scenarios.
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                        class="external-link button is-normal is-rounded is-primary">
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                          <i class="fas fa-file-pdf"></i>
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                        <span>View All Additional Cases</span>
                      </a>
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                      <p class="has-text-centered" style="margin-top: 1rem; font-size: 0.9rem; color: #666;">
                        Comprehensive examples showcasing the versatility of our approach across different domains and user requirements.
                      </p>
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                  </details>
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              </div>
            </div>

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</section>
<!-- End Use Cases Section -->

<!-- Evaluation Section -->
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      <h2 class="title is-3">Evaluation</h2>
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        <div class="column is-10">
          <div class="content has-text-justified">
            <p>
              Analysis of evolutionary algorithm parameters on transfer function optimization. (A) Average rankings over iterations for different population
              sizes (10-200), showing that larger populations achieve more stable improvements with continued iterations while smaller populations show early
              rapid gains but unstable convergence. (B) Comparison of optimization effectiveness across different computational budgets (measured by LLM API
              calls), demonstrating that medium-small populations (25-50) perform better under limited resources, while larger populations (100-200) achieve
              superior results when computational resources are abundant.
            </p>
            <img src="static/images/analysis_of_evolutionary.png" alt="Analysis of evolutionary algorithm parameters" width="100%">
          </div>
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</section>
<!-- End Evaluation Section -->

<!-- Case Study Section -->
<!-- 暂时注释掉 Case Study 章节
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      <h2 class="title is-3">Case Study</h2>
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        <div class="column is-10">
          <div class="content has-text-justified">
            <img src="static/images/case.png" alt="Case study examples" width="100%">
            <p>
              Examples of transfer functions designed using our approach. The first row showcases cases where design intent is expressed
              through text. Users input text descriptions and our approach automatically designs transfer functions to achieve high-quality volume
              rendering results corresponding to these descriptions. The second row demonstrates cases where design intent is conveyed through
              images. When users cannot clearly articulate their ideas verbally, they can directly provide reference images to express their intent in
              visual form. In each example of the second row, the small image in the bottom right corner represents the user intent in the form of a
              reference image. Reference images for (A) and (B) are from other works, (C) uses the rendered image from the third example in the
              first row, and (D) utilizes Vincent van Gogh's "The Starry Night."
            </p>
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  </div>
</section>
-->

<!--BibTex citation -->
  <section class="section" id="BibTeX">
    <div class="container is-max-desktop content">
      <h2 class="title">BibTeX</h2>
      <pre><code>@article{wang2025intuiTF,
      title        = {IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering},
      author       = {Wang, Yiyao and Pan, Bo and Wang, Ke and Liu, Han and Mao, Jinyuan and Liu, Yuxin and Zhu, Minfeng and Zhang, Bo and Chen, Weifeng and Huang, Xiuqi and Chen, Wei},
      year         = {2025},
      publisher    = {arXiv},
      url          = {https://wyysteelhead.github.io/TFEvolve.github.io/},
      doi          = {10.48550/arXiv.2506.18407}
}</code></pre>
    </div>
</section>
<!--End BibTex citation -->


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