Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

TL;DR

This paper introduces an unsupervised 3D human pose estimation method using a teacher-student framework with cycle-consistency and invariance properties, achieving state-of-the-art results without 3D annotations.

Contribution

It proposes a novel unsupervised approach combining pose-dictionary regularization, cycle-consistent architectures, and graph convolution networks for improved 3D human pose estimation.

Findings

Reduces 3D joint prediction error by 11.4% on Human3.6M

Outperforms many weakly-supervised methods

Demonstrates effectiveness on multiple datasets

Abstract

We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network. To further improve the estimation accuracy, the student network adopts a novel graph convolution network for flexibility to directly estimate the 3D coordinates. Another cycle-consistent architecture promoting 3D rotation-equivariant property is adopted to exploit geometry consistency, together with knowledge distillation from the teacher network to improve the pose estimation performance. We conduct extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D joint prediction error by 11.4% compared to state-of-the-art unsupervised methods and also outperforms many weakly-supervised methods that use side information on Human3.6M. Code will be available at https://github.com/sjtuxcx/ITES.