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

REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos