Accelerating Audio-driven 3D Facial Animation Training with a Compressive Sensing Framework

The training of end-to-end models for audio-driven 3D facial animation is often hindered by the significant computational overhead of direct loss computation on high-dimensional 3D mesh data. To address this efficiency bottleneck, we propose a novel compressed-domain training framework. Our method leverages the principles of Compressive Sensing to shift the loss computation from the high-dimensional vertex space to a compact, low-dimensional signal space. Extensive experiments on multiple state-of-the-art architectures demonstrate that our framework significantly accelerates the training process, with speedups ranging from 1.52x to a remarkable 9.57x, while maintaining generation quality comparable to SOTA methods. Our findings establish a superior solution that achieves an exceptional trade-off between computational efficiency and final animation quality.