Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitization. Recent neural rendering methods achieve high-quality dynamic clothed human reconstruction results from monocular video, but these methods cannot separate the garment surface from the body. Moreover, despite existing garment reconstruction methods based on feature curve representation demonstrating impressive results for garment reconstruction from a single image, they struggle to generate temporally consistent surfaces for the video input. To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video. We introduce a novel approach, called REC-MV, to jointly optimize the explicit feature curves and the implicit signed distance field (SDF) of the garments. Then the open garment meshes can be extracted via garment template registration in the canonical space. Experiments on multiple casually captured datasets show that our approach outperforms existing methods and can produce high-quality dynamic garment surfaces. The source code is available at https://github.com/GAP-LAB-CUHK-SZ/REC-MV.

翻译：从单目视频中重建具有开放边界的动态三维服装表面是一个重要问题，因为它为服装数字化提供了实用且低成本的解决方案。最近的神经渲染方法能够从单目视频实现高质量的动态穿衣人体重建，但这些方法无法将服装表面与人体分离。此外，尽管基于特征曲线表示的现有服装重建方法在单张图像的服装重建中展现了令人印象深刻的效果，但它们难以生成视频输入的时间一致表面。为解决上述局限性，本文将这一任务建模为从单目视频中优化三维服装特征曲线及表面重建的问题。我们提出了一种名为REC-MV的新方法，用于联合优化显式特征曲线和服装的隐式符号距离场（SDF）。随后，通过规范空间中的服装模板配准可提取开放的服装网格。在多个随意拍摄数据集上的实验表明，我们的方法优于现有方法，并能生成高质量的动态服装表面。源代码已发布于https://github.com/GAP-LAB-CUHK-SZ/REC-MV。