  Estimating Head-Related Transfer Functions (HRTFs) of arbitrary source points
is essential in immersive binaural audio rendering. Computing each individual's
HRTFs is challenging, as traditional approaches require expensive time and
computational resources, while modern data-driven approaches are data-hungry.
Especially for the data-driven approaches, existing HRTF datasets differ in
spatial sampling distributions of source positions, posing a major problem when
generalizing the method across multiple datasets. To alleviate this, we propose
a deep learning method based on a novel conditioning architecture. The proposed
method can predict an HRTF of any position by interpolating the HRTFs of known
distributions. Experimental results show that the proposed architecture
improves the model's generalizability across datasets with various coordinate
systems. Additional demonstrations show that the model robustly reconstructs
the target HRTFs from the spatially downsampled HRTFs in both quantitative and
perceptual measures.
