Drivable generalized NeRF-based head model

In recent years, the concept of digital human has attracted widespread attention from all walks of life, and the modelling of high-fidelity human bodies, heads, and hands has been intensively studied. This paper focuses on head modelling and proposes a generic head parametric model based on neural radiance fields. Specifically, we first use face recognition networks and 3D facial expression database FaceWarehouse to parameterize identity and expression semantics, respectively, and use both as conditional inputs to build a neural radiance field for the human head, thereby improving the head model’s representation ability while ensuring editing capabilities for the identity and expression of the rendered results; then, through a combination of volume rendering and neural rendering, the 3D representation of the head is rapidly rendered into the 2D plane, producing a high-fidelity image of the human head. Thanks to the well-designed loss functions and good implicit representation of the neural radiance field, our model can not only edit the identity and expression independently, but also freely modify the virtual camera position of the rendering results. It has excellent multi-view consistency, and has many applications in novel view synthesis, pose driving and more.