GRAPE: Graduated Routing for Articulated Portrait mesh Estimation

Articulated portrait mesh estimation is fundamental to 3D understanding, avatar generation, and immersive interaction. However, face-centric models suffer from the `floating head` assumption, conflating head pose with global rotation due to the lack of neck kinematics. Conversely, body-centric models lack high-fidelity facial expression capabilities. Furthermore, current methods struggle to disentangle jaw articulation from expression blendshapes, often over-relying on expressions for mouth opening. These limitations make monocular portrait recovery difficult across representation, supervision, and anatomical parameter estimation. To address these limitations, we introduce **GRAPE** (Graduated Routing for Articulated Portrait mesh Estimation). We build a **Portrait Parametric Model (PPM)** with an explicit torso-to-head kinematic chain and a canonical injection step to merge FLAME and the SMPL-X torso. We propose a**Progressive Anatomical Alignment (PAA)** network, which is composed of a pretrained portrait encoder, a graduated-mask router, and coarse-to-fine experts that follow the portrait anatomical prior. We then train this network with multi-source supervision that combines sparse anatomical keypoints, feature distillation, foreground mask constraints, and relative geometry constraints. Experiments show that GRAPE improves portrait mesh recovery quality, pose alignment, and jaw–expression disentanglement over prior methods. We also demonstrate that our method can benefit the downstream tasks of audio-driven talking-head generation and 3D portrait generation.