TaoHeadVerse: Learning Expressive Full-Head Avatars Beyond Representation Confounding

Personalized full-head avatars for mobile XR and telepresence require faithful animation and efficient rendering. Creating such avatars from one or a few portraits remains challenging because it requires completing unseen regions, preserving source identity under cross-identity driving, and modeling person-specific expression responses. We present TaoHeadVerse, a feed-forward framework that jointly estimates motion and predicts an explicit mesh-bound Gaussian avatar. To address representation confounding in joint avatar-motion learning and mixed-source training, Neutral Identity Anchoring limits identity leakage into expression components, while Adversarial Domain Alignment discourages canonical features from encoding dataset-level acquisition attributes. The predicted asset stores identity-adapted mesh blendshapes and Gaussian appearance bases, enabling animation through basis mixing without per-frame neural avatar decoding. Experiments on VFHQ, AVA256, and RenderMe-360 show improved reconstruction, identity preservation, and full-head completion with competitive expression fidelity. Given precomputed driving controls, four concurrent avatars achieve animation and rendering throughput above 60 FPS on a shared $1024\times 1024$ canvas on both mobile devices.