  In this paper, we aim to address the challenge of novel view rendering of
human performers who wear clothes with complex texture patterns using a sparse
set of camera views. Although some recent works have achieved remarkable
rendering quality on humans with relatively uniform textures using sparse
views, the rendering quality remains limited when dealing with complex texture
patterns as they are unable to recover the high-frequency geometry details that
are observed in the input views. To this end, we propose HDhuman, which uses a
human reconstruction network with a pixel-aligned spatial transformer and a
rendering network with geometry-guided pixel-wise feature integration to
achieve high-quality human reconstruction and rendering. The designed
pixel-aligned spatial transformer calculates the correlations between the input
views and generates human reconstruction results with high-frequency details.
Based on the surface reconstruction results, the geometry-guided pixel-wise
visibility reasoning provides guidance for multi-view feature integration,
enabling the rendering network to render high-quality images at 2k resolution
on novel views. Unlike previous neural rendering works that always need to
train or fine-tune an independent network for a different scene, our method is
a general framework that is able to generalize to novel subjects. Experiments
show that our approach outperforms all the prior generic or specific methods on
both synthetic data and real-world data.
