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Reconstructing personalized animatable head avatars has significantimplications in the fields of AR/VR. Existing methods for achieving explicitface control of 3D Morphable Models (3DMM) typically rely on multi-view imagesor videos of a single subject, making the reconstruction process complex.Additionally, the traditional rendering pipeline is time-consuming, limitingreal-time animation possibilities. In this paper, we introduce CVTHead, a novelapproach that generates controllable neural head avatars from a singlereference image using point-based neural rendering. CVTHead considers thesparse vertices of mesh as the point set and employs the proposedVertex-feature Transformer to learn local feature descriptors for each vertex.This enables the modeling of long-range dependencies among all the vertices.Experimental results on the VoxCeleb dataset demonstrate that CVTHead achievescomparable performance to state-of-the-art graphics-based methods. Moreover, itenables efficient rendering of novel human heads with various expressions, headposes, and camera views. These attributes can be explicitly controlled usingthe coefficients of 3DMMs, facilitating versatile and realistic animation inreal-time scenarios.

CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer