In recent years, audio-driven 3D facial animation has gained significant
attention, particularly in applications such as virtual reality, gaming, and
video conferencing. However, accurately modeling the intricate and subtle
dynamics of facial expressions remains a challenge. Most existing studies
approach the facial animation task as a single regression problem, which often
fail to capture the intrinsic inter-modal relationship between speech signals
and 3D facial animation and overlook their inherent consistency. Moreover, due
to the limited availability of 3D-audio-visual datasets, approaches learning
with small-size samples have poor generalizability that decreases the
performance. To address these issues, in this study, we propose a cross-modal
dual-learning framework, termed DualTalker, aiming at improving data usage
efficiency as well as relating cross-modal dependencies. The framework is
trained jointly with the primary task (audio-driven facial animation) and its
dual task (lip reading) and shares common audio/motion encoder components. Our
joint training framework facilitates more efficient data usage by leveraging
information from both tasks and explicitly capitalizing on the complementary
relationship between facial motion and audio to improve performance.
Furthermore, we introduce an auxiliary cross-modal consistency loss to mitigate
the potential over-smoothing underlying the cross-modal complementary
representations, enhancing the mapping of subtle facial expression dynamics.
Through extensive experiments and a perceptual user study conducted on the VOCA
and BIWI datasets, we demonstrate that our approach outperforms current
state-of-the-art methods both qualitatively and quantitatively. We have made
our code and video demonstrations available at
this https URL

通过交叉模态的双学习框架和辅助的模态一致性损失，提高数据使用效率、关联交叉模态的依赖关系，并增强微妙面部表情动力学的映射，从而在语音驱动三维面部动画中提高性能。