  This work digs into a root question in human perception: can face geometry be
gleaned from one's voices? Previous works that study this question only adopt
developments in image synthesis and convert voices into face images to show
correlations, but working on the image domain unavoidably involves predicting
attributes that voices cannot hint, including facial textures, hairstyles, and
backgrounds. We instead investigate the ability to reconstruct 3D faces to
concentrate on only geometry, which is much more physiologically grounded. We
propose our analysis framework, Cross-Modal Perceptionist, under both
supervised and unsupervised learning. First, we construct a dataset,
Voxceleb-3D, which extends Voxceleb and includes paired voices and face meshes,
making supervised learning possible. Second, we use a knowledge distillation
mechanism to study whether face geometry can still be gleaned from voices
without paired voices and 3D face data under limited availability of 3D face
scans. We break down the core question into four parts and perform visual and
numerical analyses as responses to the core question. Our findings echo those
in physiology and neuroscience about the correlation between voices and facial
structures. The work provides future human-centric cross-modal learning with
explainable foundations. See our project page:
https://choyingw.github.io/works/Voice2Mesh/index.html
