Estimation of 3D human pose from monocular image has gained considerable
attention, as a key step to several human-centric applications. However,
generalizability of human pose estimation models developed using supervision on
large-scale in-studio datasets remains questionable, as these models often
perform unsatisfactorily on unseen in-the-wild environments. Though
weakly-supervised models have been proposed to address this shortcoming,
performance of such models relies on availability of paired supervision on some
related tasks, such as 2D pose or multi-view image pairs. In contrast, we
propose a novel kinematic-structure-preserved unsupervised 3D pose estimation
framework, which is not restrained by any paired or unpaired weak supervisions.
Our pose estimation framework relies on a minimal set of prior knowledge that
defines the underlying kinematic 3D structure, such as skeletal joint
connectivity information with bone-length ratios in a fixed canonical scale.
The proposed model employs three consecutive differentiable transformations
named as forward-kinematics, camera-projection and spatial-map transformation.
This design not only acts as a suitable bottleneck stimulating effective pose
disentanglement but also yields interpretable latent pose representations
avoiding training of an explicit latent embedding to pose mapper. Furthermore,
devoid of unstable adversarial setup, we re-utilize the decoder to formalize an
energy-based loss, which enables us to learn from in-the-wild videos, beyond
laboratory settings. Comprehensive experiments demonstrate our state-of-the-art
unsupervised and weakly-supervised pose estimation performance on both
Human3.6M and MPI-INF-3DHP datasets. Qualitative results on unseen environments
further establish our superior generalization ability.

本文提出了一种采用运动学结构保持无监督学习的 3D 人体姿势估计框架，可以避免使用任何弱监督模型，并通过能量损失和运动学先验知识来训练模型，提高对新环境下的准确性和泛化能力。