  In this work, we focus on synthesizing high-fidelity novel view images for
arbitrary human performers, given a set of sparse multi-view images. It is a
challenging task due to the large variation among articulated body poses and
heavy self-occlusions. To alleviate this, we introduce an effective
generalizable framework Generalizable Model-based Neural Radiance Fields
(GM-NeRF) to synthesize free-viewpoint images. Specifically, we propose a
geometry-guided attention mechanism to register the appearance code from
multi-view 2D images to a geometry proxy which can alleviate the misalignment
between inaccurate geometry prior and pixel space. On top of that, we further
conduct neural rendering and partial gradient backpropagation for efficient
perceptual supervision and improvement of the perceptual quality of synthesis.
To evaluate our method, we conduct experiments on synthesized datasets
THuman2.0 and Multi-garment, and real-world datasets Genebody and ZJUMocap. The
results demonstrate that our approach outperforms state-of-the-art methods in
terms of novel view synthesis and geometric reconstruction.
