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.

从几何学的角度而不是语义信息来重建声音到三维面部形状的能力进行研究，通过使用声音测量 - 面部范例识别可预测的面部测量特征并将其用于指导三维面部重建，消除了无法预测的面部测量特征的影响，从而使得面部几何学可操作。在我们提出的数据集上进行模型评估，发现声音与面部几何特征的特定部位（如鼻腔和颅骨）之间存在显著相关性，为声音 - 面部相关性提供了新的视角，并可作为人类测量学科的实证研究。