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This paper introduces a dual-path modulation framework for explicitly controlling emotion in feed-forward, single-image 3D head avatar reconstruction. The method disentangles emotion from geometry and appearance by using emotion-conditioned normalization in the parametric space and capturing identity-aware visual cues. To train the model, the authors construct a time-synchronized, emotion-consistent multi-identity dataset by transferring aligned emotional dynamics across identities.
Finally, you can puppeteer photorealistic 3D head avatars with independent control over identity, articulation, and nuanced emotional expression.
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.
