GDAvatar: Geometry-Guided Dynamic Gaussian Head Avatars from Monocular Videos

Recent Gaussian head avatars achieve remarkable rendering quality and real-time performance, but coarse canonical geometry and limited motion information hinder faithful reconstruction of expression-dependent appearance details. In this paper, we propose GDAvatar, a geometry-guided dynamic Gaussian head avatar framework for monocular videos. We first optimize the canonical mesh using a vertex-bound Gaussian representation to obtain personalized geometry. Building upon the optimized geometry, we then employ a triangle-bound Gaussian representation to model dynamic Gaussian attribute offsets by jointly encoding canonical and dynamic features. An attention fusion module adaptively balances these features, enabling more accurate modeling of local non-rigid deformations. Furthermore, we construct a continuous editable Gaussian map from the learned representation, enabling flexible and high-quality texture editing. Extensive experiments on multiple datasets demonstrate that GDAvatar achieves superior rendering quality and animation fidelity compared with state-of-the-art methods, while naturally supporting high-quality texture editing.