Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning

TL;DR

This paper introduces a semi- and weakly-supervised deep learning approach for hand pose estimation that leverages synthetic and real datasets by using an intermediate segmentation representation to improve accuracy.

Contribution

It presents a novel training method that combines synthetic and real data through an intermediate segmentation, reducing domain shift and enhancing hand pose estimation accuracy.

Findings

Reduces joint estimation error by 15.7% on NYU dataset.

Utilizes an intermediate segmentation to bridge synthetic and real data domains.

Improves hand pose estimation accuracy with semi/weakly-supervised learning.

Abstract

We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides useful cues for reasoning about joint locations. The mapping from raw depth to segmentation maps is learned in a semi/weakly-supervised way from two different datasets: (i) a synthetic dataset created through a rendering pipeline including densely labeled ground truth (pixelwise segmentations); and (ii) a dataset with real images for which ground truth joint positions are available, but not dense segmentations. Loss for training on real images is generated from a patch-wise restoration process, which aligns tentative segmentation maps with a large dictionary of synthetic poses. The underlying premise is that the domain shift between synthetic and real data is smaller in the intermediate representation, where labels carry geometric and topological meaning, than in the raw input domain. Experiments on the NYU dataset show that the proposed training method decreases error on joints over direct regression of joints from depth data by 15.7%.