标题： 你所想即你所得：通过多模态大语言模型连接用户意图与传输函数设计 显示英文标题

标题： What You Think Is What You Get: Bridge User Intent and Transfer Function Design through Multimodal Large Language Models

摘要： 直接体积渲染（DVR）是可视化体积数据的基本技术，传递函数（TFs）在提取有意义的结构中起着关键作用。 然而，由于用户意图与TF参数空间之间的语义差距，设计有效的TF仍然不直观。 研究人员已经开发了许多TF优化方法来弥合这一差距。 然而，现有方法仍然面临两个挑战：探索空间大和泛化能力弱。 为了解决这些问题，我们提出了 What You Think is What You Get（WYTWYG）框架，该框架利用多模型大语言模型（MLLMs）根据用户意图引导TF优化。 具体来说，我们首先介绍了一种新的TF优化方法，包含两个核心组件：（1）基于进化的方法用于有效探索TF空间，（2）基于MLLMs的体积渲染质量评估器，以提供可泛化的视觉指导。 我们进一步提出了一种基于该方法的TF交互设计系统。 我们通过三个案例研究展示了我们框架的通用适用性，并通过大量实验验证了每个组件的有效性。 我们的代码可在以下地址获取：https://github.com/wyysteelhead/TFevolve。 显示英文摘要

摘要： Direct volume rendering (DVR) is a fundamental technique for visualizing volumetric data, with transfer functions (TFs) playing 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. Researchers have developed numerous TF optimization methods to bridge this gap. However, existing methods still face two challenges: large exploration space and weak generalizability. To address these issues, we propose What You Think is What You Get (WYTWYG) framework, which leveraging Multi-model Large Language Models (MLLMs) to guide the TF optimization based on user intent. Specifically, we first introduce a novel TF optimization approach comprising two core components: (1) an evolution-based explorer for effective exploration of the TF space, and (2) a volume rendering quality evaluator based on MLLMs to provide generalizable visual guidance. We further propose a TF interactive design system based on this approach. We demonstrate the general applicability of our framework through three case studies, and validate the effectiveness of each component through extensive experiments. Our code is available at: https://github.com/wyysteelhead/TFevolve.