3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow
Felix Taubner Prashant Raina Mathieu Tuli Eu Wern Teh Chul Lee Jinmiao Huang
When working with 3D facial data, improving fidelity and avoiding the uncanny
valley effect is critically dependent on accurate 3D facial performance
capture. Because such methods are expensive and due to the widespread
availability of 2D videos, recent methods have focused on how to perform
monocular 3D face tracking. However, these methods often fall short in
capturing precise facial movements due to limitations in their network
architecture, training, and evaluation processes. Addressing these challenges,
we propose a novel face tracker, FlowFace, that introduces an innovative 2D
alignment network for dense per-vertex alignment. Unlike prior work, FlowFace
is trained on high-quality 3D scan annotations rather than weak supervision or
synthetic data. Our 3D model fitting module jointly fits a 3D face model from
one or many observations, integrating existing neutral shape priors for
enhanced identity and expression disentanglement and per-vertex deformations
for detailed facial feature reconstruction. Additionally, we propose a novel
metric and benchmark for assessing tracking accuracy. Our method exhibits
superior performance on both custom and publicly available benchmarks. We
further validate the effectiveness of our tracker by generating high-quality 3D
data from 2D videos, which leads to performance gains on downstream tasks.