FoldMatch: Accurate and High Fidelity Garment Fitting Onto 3D Scans

Sk Aziz Ali, Sikang Yan, Wolfgang Dornisch, Didier Stricker

In: IEEE Signal Processing Society. IEEE International Conference on Image Processing (ICIP-2020) October 25-28 Abu Dhabi United Arab Emirates IEEE 10/2020.


In this paper, we propose a new template fitting method that can capture fine details of garments in target 3D scans of dressed human bodies. Matching the high fidelity details of such loose/tight-fit garments is a challenging task as they express intricate folds, creases, wrinkle patterns, and other high fidelity surface details. Our proposed method of non-rigid shape fitting – FoldMatch – uses physics-based particle dynamics to explicitly model the deformation of loose-fit garments and wrinkle vector fields for capturing clothing details. The 3D scan point cloud behaves as a collection of astrophysical particles, which attracts the points in template mesh and defines the template motion model. We use this point-based motion model to derive regularized deformation gradients for the template mesh. We show the parameterization of the wrinkle vector fields helps in the accurate shape fitting. Our method shows better performance than state-of-the-art methods. We define several deformation and shape matching quality measurement metrics to evaluate FoldMatch on synthetic and real data sets.


Ali2021ICIP_foldmatch.pdf (pdf, 15 MB ) AliPresentationICIP2021.mp4 (mp4, 38 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz