  We present HumanNeRF-SE, a simple yet effective method that synthesizes
diverse novel pose images with simple input. Previous HumanNeRF works require a
large number of optimizable parameters to fit the human images. Instead, we
reload these approaches by combining explicit and implicit human
representations to design both generalized rigid deformation and specific
non-rigid deformation. Our key insight is that explicit shape can reduce the
sampling points used to fit implicit representation, and frozen blending
weights from SMPL constructing a generalized rigid deformation can effectively
avoid overfitting and improve pose generalization performance. Our architecture
involving both explicit and implicit representation is simple yet effective.
Experiments demonstrate our model can synthesize images under arbitrary poses
with few-shot input and increase the speed of synthesizing images by 15 times
through a reduction in computational complexity without using any existing
acceleration modules. Compared to the state-of-the-art HumanNeRF studies,
HumanNeRF-SE achieves better performance with fewer learnable parameters and
less training time.
