  Recent neural rendering approaches for human activities achieve remarkable
view synthesis results, but still rely on dense input views or dense training
with all the capture frames, leading to deployment difficulty and inefficient
training overload. However, existing advances will be ill-posed if the input is
both spatially and temporally sparse. To fill this gap, in this paper we
propose a few-shot neural human rendering approach (FNHR) from only sparse RGBD
inputs, which exploits the temporal and spatial redundancy to generate
photo-realistic free-view output of human activities. Our FNHR is trained only
on the key-frames which expand the motion manifold in the input sequences. We
introduce a two-branch neural blending to combine the neural point render and
classical graphics texturing pipeline, which integrates reliable observations
over sparse key-frames. Furthermore, we adopt a patch-based adversarial
training process to make use of the local redundancy and avoids over-fitting to
the key-frames, which generates fine-detailed rendering results. Extensive
experiments demonstrate the effectiveness of our approach to generate
high-quality free view-point results for challenging human performances under
the sparse setting.
