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：音频驱动说话头像合成是虚拟人类相关应用中的热门研究方向。然而，现有方法存在灵活性和效率低下的显著局限，需要昂贵的端到端训练才能将引导视频中的情感迁移至说话头像预测结果。本文提出音频驱动说话头像情感适应方法（EAT），通过参数高效适应方式，以低成本高效方式将情感无关的说话头像模型转化为情感可控模型。该方法基于预训练的情感无关说话头像变换器，引入三种轻量级适应模块（深度情感提示、情感形变网络和情感适应模块），从不同角度实现精准逼真的情感控制。实验表明，该方法在包括LRW和MEAD在内的广泛使用基准上达到了最先进性能。此外，参数高效适应模块展现出卓越的泛化能力，即使在情感训练视频稀缺或完全缺失的场景下仍能保持性能。项目网站：https://yuangan.github.io/eat/