Learned Vertex Descent: A New Direction for 3D Human Model Fitting

Abstract

"We propose a novel optimization-based paradigm for 3D human shape fitting on images. In contrast to existing approaches that directly regress the parameters of a low-dimensional statistical body model (e.g. SMPL) from input images, we propose training a deep network that, given solely image features and an unfit mesh, predicts the directions of the vertices towards the 3D body mesh. At inference, we employ this network, dubbed LVD, within a gradient-descent optimization pipeline until its convergence, which typically occurs in a fraction of a second even when initializing all vertices into a single point. An exhaustive evaluation demonstrates that our approach is able to capture the underlying body of clothed people with very different body shapes, achieving a significant improvement compared to state-of-the-art. Additionally, the proposed formulation can generalize to other sources of input data, which we experimentally show on fitting 3D scans of full bodies and hands."