  Talking head generation aims to generate faces that maintain the identity
information of the source image and imitate the motion of the driving image.
Most pioneering methods rely primarily on 2D representations and thus will
inevitably suffer from face distortion when large head rotations are
encountered. Recent works instead employ explicit 3D structural representations
or implicit neural rendering to improve performance under large pose changes.
Nevertheless, the fidelity of identity and expression is not so desirable,
especially for novel-view synthesis. In this paper, we propose HiDe-NeRF, which
achieves high-fidelity and free-view talking-head synthesis. Drawing on the
recently proposed Deformable Neural Radiance Fields, HiDe-NeRF represents the
3D dynamic scene into a canonical appearance field and an implicit deformation
field, where the former comprises the canonical source face and the latter
models the driving pose and expression. In particular, we improve fidelity from
two aspects: (i) to enhance identity expressiveness, we design a generalized
appearance module that leverages multi-scale volume features to preserve face
shape and details; (ii) to improve expression preciseness, we propose a
lightweight deformation module that explicitly decouples the pose and
expression to enable precise expression modeling. Extensive experiments
demonstrate that our proposed approach can generate better results than
previous works. Project page: https://www.waytron.net/hidenerf/
