  This work focuses on the analysis that whether 3D face models can be learned
from only the speech inputs of speakers. Previous works for cross-modal face
synthesis study image generation from voices. However, image synthesis includes
variations such as hairstyles, backgrounds, and facial textures, that are
arguably irrelevant to voice or without direct studies to show correlations. We
instead investigate the ability to reconstruct 3D faces to concentrate on only
geometry, which is more physiologically grounded. We propose both the
supervised learning and unsupervised learning frameworks. Especially we
demonstrate how unsupervised learning is possible in the absence of a direct
voice-to-3D-face dataset under limited availability of 3D face scans when the
model is equipped with knowledge distillation. To evaluate the performance, we
also propose several metrics to measure the geometric fitness of two 3D faces
based on points, lines, and regions. We find that 3D face shapes can be
reconstructed from voices. Experimental results suggest that 3D faces can be
reconstructed from voices, and our method can improve the performance over the
baseline. The best performance gains (15% - 20%) on ear-to-ear distance ratio
metric (ER) coincides with the intuition that one can roughly envision whether
a speaker's face is overall wider or thinner only from a person's voice. See
our project page for codes and data.
