Rethinking Voice-Face Correlation: A Geometry View
Xiang Li Yandong Wen Muqiao Yang Jinglu Wang Rita Singh Bhiksha Raj
Previous works on voice-face matching and voice-guided face synthesis
demonstrate strong correlations between voice and face, but mainly rely on
coarse semantic cues such as gender, age, and emotion. In this paper, we aim to
investigate the capability of reconstructing the 3D facial shape from voice
from a geometry perspective without any semantic information. We propose a
voice-anthropometric measurement (AM)-face paradigm, which identifies
predictable facial AMs from the voice and uses them to guide 3D face
reconstruction. By leveraging AMs as a proxy to link the voice and face
geometry, we can eliminate the influence of unpredictable AMs and make the face
geometry tractable. Our approach is evaluated on our proposed dataset with
ground-truth 3D face scans and corresponding voice recordings, and we find
significant correlations between voice and specific parts of the face geometry,
such as the nasal cavity and cranium. Our work offers a new perspective on
voice-face correlation and can serve as a good empirical study for
anthropometry science.