Total: 1
High-fidelity immersive audio experiences demand personalized head-related transfer functions (HRTFs), because generic HRTFs yield inaccurate perceptual localization. Neural fields (NFs) that take the sound source direction together with a compact set of subject-specific parameters to generate personalized HRTFs have achieved accurate HRTF spatial upsampling. These parameters are typically optimized from measured HRTFs, which requires an intricate measurement process in an anechoic chamber. Towards more accessible HRTF personalization, we propose a sim-to-real NF (S2RNF) that converts HRTFs simulated from an individual's 3D head mesh into their realistic counterparts. Specifically, we infer S2RNF's subject-specific parameters from the simulated HRTFs, and use those parameters to predict the realistic HRTFs. Our experiments confirm that S2RNF outperforms the original simulations and existing sim-to-real methods.