Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation
This enables on-device, real-time facial animation for applications like game development, addressing a resource constraint issue, though it is incremental as it builds on existing knowledge distillation techniques.
The paper tackled the problem of creating high-quality, real-time facial animation models for on-device use by reducing model size and latency, achieving a memory footprint as low as 3.4 MB and required audio context down to 81 ms while maintaining animation quality.
The training of high-quality, robust machine learning models for speech-driven 3D facial animation requires a large, diverse dataset of high-quality audio-animation pairs. To overcome the lack of such a dataset, recent work has introduced large pre-trained speech encoders that are robust to variations in the input audio and, therefore, enable the facial animation model to generalize across speakers, audio quality, and languages. However, the resulting facial animation models are prohibitively large and lend themselves only to offline inference on a dedicated machine. In this work, we explore on-device, real-time facial animation models in the context of game development. We overcome the lack of large datasets by using hybrid knowledge distillation with pseudo-labeling. Given a large audio dataset, we employ a high-performing teacher model to train very small student models. In contrast to the pre-trained speech encoders, our student models only consist of convolutional and fully-connected layers, removing the need for attention context or recurrent updates. In our experiments, we demonstrate that we can reduce the memory footprint to up to 3.4 MB and required future audio context to up to 81 ms while maintaining high-quality animations. This paves the way for on-device inference, an important step towards realistic, model-driven digital characters.