Audio-driven talking-head synthesis is a popular research topic for virtual human-related applications. However, the inflexibility and inefficiency of existing methods, which necessitate expensive end-to-end training to transfer emotions from guidance videos to talking-head predictions, are significant limitations. In this work, we propose the Emotional Adaptation for Audio-driven Talking-head (EAT) method, which transforms emotion-agnostic talking-head models into emotion-controllable ones in a cost-effective and efficient manner through parameter-efficient adaptations. Our approach utilizes a pretrained emotion-agnostic talking-head transformer and introduces three lightweight adaptations (the Deep Emotional Prompts, Emotional Deformation Network, and Emotional Adaptation Module) from different perspectives to enable precise and realistic emotion controls. Our experiments demonstrate that our approach achieves state-of-the-art performance on widely-used benchmarks, including LRW and MEAD. Additionally, our parameter-efficient adaptations exhibit remarkable generalization ability, even in scenarios where emotional training videos are scarce or nonexistent. Project website: https://yuangan.github.io/eat/

翻译：音频驱动的说话头合成是虚拟人类相关应用中一个热门的研究课题。然而，现有方法存在灵活性和效率低下的显著限制，它们需要昂贵的端到端训练才能将引导视频中的情感迁移到说话头预测中。在本工作中，我们提出了情感适应音频驱动说话头（EAT）方法，通过参数高效的适应方式，将情感无关的说话头模型转化为情感可控的模型，以实现经济高效且高效的操作。我们的方法利用一个预训练的情感无关说话头Transformer，并从不同角度引入了三种轻量级适应模块（深度情感提示、情感变形网络和情感适应模块），以实现精确且逼真的情感控制。我们的实验表明，该方法在广泛使用的基准测试（包括LRW和MEAD）上达到了最先进的性能。此外，即使在情感训练视频稀缺或不存在的情况下，我们的参数高效适应模块也展现出显著的泛化能力。项目网站：https://yuangan.github.io/eat/