  We propose a Few-shot Dynamic Neural Radiance Field (FDNeRF), the first
NeRF-based method capable of reconstruction and expression editing of 3D faces
based on a small number of dynamic images. Unlike existing dynamic NeRFs that
require dense images as input and can only be modeled for a single identity,
our method enables face reconstruction across different persons with few-shot
inputs. Compared to state-of-the-art few-shot NeRFs designed for modeling
static scenes, the proposed FDNeRF accepts view-inconsistent dynamic inputs and
supports arbitrary facial expression editing, i.e., producing faces with novel
expressions beyond the input ones. To handle the inconsistencies between
dynamic inputs, we introduce a well-designed conditional feature warping (CFW)
module to perform expression conditioned warping in 2D feature space, which is
also identity adaptive and 3D constrained. As a result, features of different
expressions are transformed into the target ones. We then construct a radiance
field based on these view-consistent features and use volumetric rendering to
synthesize novel views of the modeled faces. Extensive experiments with
quantitative and qualitative evaluation demonstrate that our method outperforms
existing dynamic and few-shot NeRFs on both 3D face reconstruction and
expression editing tasks. Code is available at
https://github.com/FDNeRF/FDNeRF.
