Automatic Transfer Function Design via
MLLM-Assisted 2D Semantic Decomposition

Designing transfer functions (TFs) for direct volume rendering is a fundamental yet labor-intensive task, as users need to carefully explore the relationship between voxel attributes and visual outcomes to reveal structures of interest. Despite advances in learning-based approaches, many existing methods depend on repeated user input. Furthermore, many methods relying on conventional data value-based TFs, which use voxel intensity or gradient information, often struggle to separate voxels into meaningful semantic groups. To address these problems, we present a framework that enables automatic TF design via multimodal large language model (MLLM)-assisted 2D semantic decomposition, requiring only minimal user input, namely the volume name and a projection axis. Starting from a single maximum intensity projection of the volume, our method transforms it into a semantic segmentation mask using an MLLM, which is then lifted into 3D as a sparse set of reliable voxel cues. To convert these sparse cues into a complete semantic representation, we introduce a voxel classification network trained with a confidence-aware propagation loss, resulting in dense voxel-level semantic assignments that are readily translated into TFs. Despite relying solely on single-image segmentation, our method achieves precise part-level separation. Evaluation on a range of volume datasets shows that ours produces better semantic renderings while significantly reducing the effort and time required for TF design compared to state-of-the-art techniques.