  Transfer function design is crucial in volume rendering, as it directly
influences the visual representation and interpretation of volumetric data.
However, creating effective transfer functions that align with users' visual
objectives is often challenging due to the complex parameter space and the
semantic gap between transfer function values and features of interest within
the volume. In this work, we propose a novel approach that leverages recent
advancements in language-vision models to bridge this semantic gap. By
employing a fully differentiable rendering pipeline and an image-based loss
function guided by language descriptions, our method generates transfer
functions that yield volume-rendered images closely matching the user's intent.
We demonstrate the effectiveness of our approach in creating meaningful
transfer functions from simple descriptions, empowering users to intuitively
express their desired visual outcomes with minimal effort. This advancement
streamlines the transfer function design process and makes volume rendering
more accessible to a wider range of users.
