  We propose CrossHuman, a novel method that learns cross-guidance from
parametric human model and multi-frame RGB images to achieve high-quality 3D
human reconstruction. To recover geometry details and texture even in invisible
regions, we design a reconstruction pipeline combined with tracking-based
methods and tracking-free methods. Given a monocular RGB sequence, we track the
parametric human model in the whole sequence, the points (voxels) corresponding
to the target frame are warped to reference frames by the parametric body
motion. Guided by the geometry priors of the parametric body and spatially
aligned features from RGB sequence, the robust implicit surface is fused.
Moreover, a multi-frame transformer (MFT) and a self-supervised warp refinement
module are integrated to the framework to relax the requirements of parametric
body and help to deal with very loose cloth. Compared with previous works, our
CrossHuman enables high-fidelity geometry details and texture in both visible
and invisible regions and improves the accuracy of the human reconstruction
even under estimated inaccurate parametric human models. The experiments
demonstrate that our method achieves state-of-the-art (SOTA) performance.
