Reconstructing 3D clothed human involves creating a detailed geometry of
individuals in clothing, with applications ranging from virtual try-on, movies,
to games. To enable practical and widespread applications, recent advances
propose to generate a clothed human from an RGB image. However, they struggle
to reconstruct detailed and robust avatars simultaneously. We empirically find
that the high-frequency (HF) and low-frequency (LF) information from a
parametric model has the potential to enhance geometry details and improve
robustness to noise, respectively. Based on this, we propose HiLo, namely
clothed human reconstruction with high- and low-frequency information, which
contains two components. 1) To recover detailed geometry using HF information,
we propose a progressive HF Signed Distance Function to enhance the detailed 3D
geometry of a clothed human. We analyze that our progressive learning manner
alleviates large gradients that hinder model convergence. 2) To achieve robust
reconstruction against inaccurate estimation of the parametric model by using
LF information, we propose a spatial interaction implicit function. This
function effectively exploits the complementary spatial information from a
low-resolution voxel grid of the parametric model. Experimental results
demonstrate that HiLo outperforms the state-of-the-art methods by 10.43% and
9.54% in terms of Chamfer distance on the Thuman2.0 and CAPE datasets,
respectively. Additionally, HiLo demonstrates robustness to noise from the
parametric model, challenging poses, and various clothing styles.

通过使用高频率和低频率信息，提出了 HiLo 方法来从 RGB 图像中重建穿着衣物的人体，用于虚拟试衣、电影和游戏等应用。实验结果表明，HiLo 方法在 Thuman2.0 和 CAPE 数据集上的 Chamfer 距离方面的优势达到 10.43% 和 9.54%，同时对来自参数化模型的噪声、挑战姿势和不同服装风格表现出鲁棒性。