让3D头像表情可独立控制，实现自然情感表达。
Giving Faces Their Feelings Back: Explicit Emotion Control for Feedforward Single-Image 3D Head Avatars
- 用双路调制机制将情绪作为独立信号注入现有模型
- 支持跨身份情感迁移与平滑情绪插值，保持还原精度
- 适合需要精细情感控制的虚拟人、影视特效等场景
我们提出一种前馈式单图3D头像重建框架，实现显式情绪控制。不同于以往将情绪隐含于几何或外观中的方法，我们将情绪视为可独立操作的第一类控制信号。通过双路径调制机制，在不修改原有架构的前提下，将情绪注入现有模型：几何调制在原始参数空间进行情绪条件归一化，解耦情绪状态与语音驱动的嘴型变化；外观调制捕捉超越几何的身份感知、情绪依赖视觉特征。为支持该设定下的学习，我们构建了一个时间同步、情绪一致的多身份数据集，通过跨身份转移对齐的情感动态生成。集成到多个顶尖骨干网络后，本框架在保持重建与重演保真度的同时，实现了可控情感迁移、解耦操作与平滑情绪插值，推动了更具表现力与可扩展性的3D头像发展。
原文摘要 · Abstract (English)
We present a framework for explicit emotion control in feed-forward, single-image 3D head avatar reconstruction. Unlike existing pipelines where emotion is implicitly entangled with geometry or appearance, we treat emotion as a first-class control signal that can be manipulated independently and consistently across identities. Our method injects emotion into existing feed-forward architectures via a dual-path modulation mechanism without modifying their core design. Geometry modulation performs emotion-conditioned normalization in the original parametric space, disentangling emotional state from speech-driven articulation, while appearance modulation captures identity-aware, emotion-dependent visual cues beyond geometry. To enable learning under this setting, we construct a time-synchronized, emotion-consistent multi-identity dataset by transferring aligned emotional dynamics across identities. Integrated into multiple state-of-the-art backbones, our framework preserves reconstruction and reenactment fidelity while enabling controllable emotion transfer, disentangled manipulation, and smooth emotion interpolation, advancing expressive and scalable 3D head avatars.
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