EmoTaG：基于高斯点云的情绪感知谈话头合成与少样本个性化
EmoTaG: Emotion-Aware Talking Head Synthesis on Gaussian Splatting with Few-Shot Personalization
AI总结：
本文提出EmoTaG框架，通过结构化FLAME参数空间改进运动稳定性，结合门控残差运动网络实现情绪感知的高质量谈话头合成。
摘要：
音频驱动的3D谈话头合成在NeRF和3DGS的推动下迅速发展。通过利用丰富的预训练先验，少样本方法能从几秒视频中实现即时个性化。然而，在表达性面部运动下，现有少样本方法常出现几何不稳定和音频-情绪不匹配问题。本文提出EmoTaG，基于预训练与适应范式构建少样本情绪感知3D谈话头合成框架。关键创新是将运动预测改用结构化FLAME参数空间而非直接变形3D高斯点云，引入显式几何先验提升运动稳定性。在此基础上，提出门控残差运动网络（GRMN），从音频中捕捉情绪语调并补充头部姿态和上半脸线索，实现表达力和连贯性的运动生成。大量实验表明，EmoTaG在情绪表达、唇同步、视觉真实性和运动稳定性方面均达到最先进的性能。
英文摘要：
Audio-driven 3D talking head synthesis has advanced rapidly with Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). By leveraging rich pre-trained priors, few-shot methods enable instant personalization from just a few seconds of video. However, under expressive facial motion, existing few-shot approaches often suffer from geometric instability and audio-emotion mismatch, highlighting the need for more effective emotion-aware motion modeling. In this work, we present EmoTaG, a few-shot emotion-aware 3D talking head synthesis framework built on the Pretrain-and-Adapt paradigm. Our key insight is to reformulate motion prediction in a structured FLAME parameter space rather than directly deforming 3D Gaussians, thereby introducing explicit geometric priors that improve motion stability. Building upon this, we propose a Gated Residual Motion Network (GRMN), which captures emotional prosody from audio while supplementing head pose and upper-face cues absent from audio, enabling expressive and coherent motion generation. Extensive experiments demonstrate that EmoTaG achieves state-of-the-art performance in emotional expressiveness, lip synchronization, visual realism, and motion stability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
