IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering
This addresses a domain-specific problem in visualization for researchers and practitioners dealing with volumetric data, offering an incremental improvement over existing TF optimization methods.
The paper tackles the problem of designing effective transfer functions for direct volume rendering, which is unintuitive due to a semantic gap between user intent and parameter space, by proposing IntuiTF, a framework that uses Multimodal Large Language Models to guide optimization, resulting in improved exploration and generalizability as demonstrated through case studies and experiments.
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: (1) an evolution-driven explorer for effective exploration of the TF space, and (2) 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. We strongly recommend readers check our cases, demo video, and source code at: https://github.com/wyysteelhead/IntuiTF