Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from an image to a 3D pose, which ignores the dependencies between human joints, or model these dependencies via a max-margin structured learning framework, which involves a high computational cost at inference time. In this paper, we introduce a Deep Learning regression architecture for structured prediction of 3D human pose from monocular images or 2D joint location heatmaps that relies on an overcomplete autoencoder to learn a high-dimensional latent pose representation and accounts for joint dependencies. We further propose an efficient Long Short-Term Memory network to enforce temporal consistency on 3D pose predictions. We demonstrate that our approach achieves state-of-the-art performance both in terms of structure preservation and prediction accuracy on standard 3D human pose estimation benchmarks.

中文翻译：

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使用深度神经网络学习 3D 人体姿势的潜在表示

最近的单目 3D 姿态估计方法依赖于深度学习。他们要么训练卷积神经网络直接从图像回归到 3D 姿势，这忽略了人体关节之间的依赖关系，要么通过最大边距结构化学习框架对这些依赖关系进行建模，这在推理时涉及高计算成本。在本文中，我们介绍了一种深度学习回归架构，用于根据单目图像或 2D 关节位置热图对 3D 人体姿势进行结构化预测，该架构依赖于过度完备的自动编码器来学习高维潜在姿势表示并解释关节依赖性。我们进一步提出了一个有效的长期短期记忆网络来加强 3D 姿势预测的时间一致性。