Audio-Driven 3D Facial Animation from In-the-Wild Videos
This addresses the limited generalization in audio-driven 3D facial animation for applications like virtual avatars or entertainment, though it is incremental as it builds on existing 3D reconstruction methods.
The paper tackles the problem of generating 3D facial animation from audio by using in-the-wild 2D talking-head videos for training, resulting in improved generalization and high-fidelity lip synchronization with distinct personal styles.
Given an arbitrary audio clip, audio-driven 3D facial animation aims to generate lifelike lip motions and facial expressions for a 3D head. Existing methods typically rely on training their models using limited public 3D datasets that contain a restricted number of audio-3D scan pairs. Consequently, their generalization capability remains limited. In this paper, we propose a novel method that leverages in-the-wild 2D talking-head videos to train our 3D facial animation model. The abundance of easily accessible 2D talking-head videos equips our model with a robust generalization capability. By combining these videos with existing 3D face reconstruction methods, our model excels in generating consistent and high-fidelity lip synchronization. Additionally, our model proficiently captures the speaking styles of different individuals, allowing it to generate 3D talking-heads with distinct personal styles. Extensive qualitative and quantitative experimental results demonstrate the superiority of our method.