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.

翻译：三维穿衣人体重建旨在生成包含服装细节的个人三维几何模型，在虚拟试衣、影视制作及游戏领域具有广泛应用。为实现实用化与普适性，最新研究尝试从单张RGB图像重建穿衣人体，但现有方法难以同时保证几何细节的精确性与姿态鲁棒性。实证研究发现，参数化模型中的高频信息能增强几何细节，低频信息则可提升噪声鲁棒性。基于此，我们提出HiLo方法（高频与低频信息融合的穿衣人体重建），包含两个核心组件：1）针对几何细节恢复，提出渐进式高频有符号距离函数，通过渐进学习策略缓解梯度陡峭导致的模型收敛困难，有效增强穿衣人体三维几何细节；2）针对参数模型参数估计误差导致的鲁棒性问题，提出空间交互隐式函数，通过挖掘参数模型低分辨率体素网格的互补空间信息实现鲁棒重建。实验结果表明，在Thuman2.0与CAPE数据集上，HiLo的Chamfer距离指标分别超过现有最优方法10.43%与9.54%。此外，HiLo对参数模型噪声、复杂姿态及多样化服装风格均展现出强鲁棒性。