Self-Supervised 3D Hand Pose Estimation from monocular RGB via
Contrastive Learning
- URL: http://arxiv.org/abs/2106.05953v1
- Date: Thu, 10 Jun 2021 17:48:57 GMT
- Title: Self-Supervised 3D Hand Pose Estimation from monocular RGB via
Contrastive Learning
- Authors: Adrian Spurr, Aneesh Dahiya, Xucong Zhang, Xi Wang, Otmar Hilliges
- Abstract summary: We propose a new self-supervised method for the structured regression task of 3D hand pose estimation.
We experimentally investigate the impact of invariant and equivariant contrastive objectives.
We show that a standard ResNet-152, trained on additional unlabeled data, attains an improvement of $7.6%$ in PA-EPE on FreiHAND.
- Score: 50.007445752513625
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Acquiring accurate 3D annotated data for hand pose estimation is a
notoriously difficult problem. This typically requires complex multi-camera
setups and controlled conditions, which in turn creates a domain gap that is
hard to bridge to fully unconstrained settings. Encouraged by the success of
contrastive learning on image classification tasks, we propose a new
self-supervised method for the structured regression task of 3D hand pose
estimation. Contrastive learning makes use of unlabeled data for the purpose of
representation learning via a loss formulation that encourages the learned
feature representations to be invariant under any image transformation. For 3D
hand pose estimation, it too is desirable to have invariance to appearance
transformation such as color jitter. However, the task requires equivariance
under affine transformations, such as rotation and translation. To address this
issue, we propose an equivariant contrastive objective and demonstrate its
effectiveness in the context of 3D hand pose estimation. We experimentally
investigate the impact of invariant and equivariant contrastive objectives and
show that learning equivariant features leads to better representations for the
task of 3D hand pose estimation. Furthermore, we show that a standard
ResNet-152, trained on additional unlabeled data, attains an improvement of
$7.6\%$ in PA-EPE on FreiHAND and thus achieves state-of-the-art performance
without any task specific, specialized architectures.
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