  In this work we develop a generalizable and efficient Neural Radiance Field
(NeRF) pipeline for high-fidelity free-viewpoint human body synthesis under
settings with sparse camera views. Though existing NeRF-based methods can
synthesize rather realistic details for human body, they tend to produce poor
results when the input has self-occlusion, especially for unseen humans under
sparse views. Moreover, these methods often require a large number of sampling
points for rendering, which leads to low efficiency and limits their real-world
applicability. To address these challenges, we propose a Geometry-guided
Progressive NeRF (GP-NeRF). In particular, to better tackle self-occlusion, we
devise a geometry-guided multi-view feature integration approach that utilizes
the estimated geometry prior to integrate the incomplete information from input
views and construct a complete geometry volume for the target human body.
Meanwhile, for achieving higher rendering efficiency, we introduce a
progressive rendering pipeline through geometry guidance, which leverages the
geometric feature volume and the predicted density values to progressively
reduce the number of sampling points and speed up the rendering process.
Experiments on the ZJU-MoCap and THUman datasets show that our method
outperforms the state-of-the-arts significantly across multiple generalization
settings, while the time cost is reduced > 70% via applying our efficient
progressive rendering pipeline.
