  In this paper, we propose SelfNeRF, an efficient neural radiance field based
novel view synthesis method for human performance. Given monocular
self-rotating videos of human performers, SelfNeRF can train from scratch and
achieve high-fidelity results in about twenty minutes. Some recent works have
utilized the neural radiance field for dynamic human reconstruction. However,
most of these methods need multi-view inputs and require hours of training,
making it still difficult for practical use. To address this challenging
problem, we introduce a surface-relative representation based on
multi-resolution hash encoding that can greatly improve the training speed and
aggregate inter-frame information. Extensive experimental results on several
different datasets demonstrate the effectiveness and efficiency of SelfNeRF to
challenging monocular videos.
