  Face reenactment is challenging due to the need to establish dense
correspondence between various face representations for motion transfer. Recent
studies have utilized Neural Radiance Field (NeRF) as fundamental
representation, which further enhanced the performance of multi-view face
reenactment in photo-realism and 3D consistency. However, establishing dense
correspondence between different face NeRFs is non-trivial, because implicit
representations lack ground-truth correspondence annotations like mesh-based 3D
parametric models (e.g., 3DMM) with index-aligned vertexes. Although aligning
3DMM space with NeRF-based face representations can realize motion control, it
is sub-optimal for their limited face-only modeling and low identity fidelity.
Therefore, we are inspired to ask: Can we learn the dense correspondence
between different NeRF-based face representations without a 3D parametric model
prior? To address this challenge, we propose a novel framework, which adopts
tri-planes as fundamental NeRF representation and decomposes face tri-planes
into three components: canonical tri-planes, identity deformations, and motion.
In terms of motion control, our key contribution is proposing a Plane
Dictionary (PlaneDict) module, which efficiently maps the motion conditions to
a linear weighted addition of learnable orthogonal plane bases. To the best of
our knowledge, our framework is the first method that achieves one-shot
multi-view face reenactment without a 3D parametric model prior. Extensive
experiments demonstrate that we produce better results in fine-grained motion
control and identity preservation than previous methods.
