Implicitpca: Implicitly-Proxied Parametric Encoding for Collision-Aware Garment Reconstruction

Abstract

The emerging remote collaboration in a virtual environment calls for the need for high-fidelity 3D human reconstruction from single image.To deal with the challenges of cloth details and topologies, parametric models are widely used as explicit priors. While they often lack of fine details from the image.Neural implicit approaches generate accurate details but are typically limited to closed surfaces.In addition, physically correct reconstructions, e.g. collision-free, is crucial but often ignored in prior works.We present ImplicitPCA, a parametric SDF network that closely couples parametric encoding with implicit functions, to enjoy the fine details brought by implicit reconstruction while maintaining correct open surfaces.We introduce a fast collision-aware regression network to ensure physically-correct estimation.During inference, an iterative routine is applied to align the garment to the 2D landmarks and fit with the collision-aware cloth SDF.The experiments on the public dataset and in-the-wild images demonstrate our outperformance.

Keywords: Garment Reconstruction, Implicit and Explicit Representation, Collision Aware, Parameterized Generation, Optimization