Most recent approaches to monocular 3D pose estimation rely on Deep Learning.
They either train a Convolutional Neural Network to directly regress from image
to 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 that relies on an
overcomplete auto-encoder to learn a high-dimensional latent pose
representation and account for joint dependencies. We demonstrate that our
approach outperforms state-of-the-art ones both in terms of structure
preservation and prediction accuracy.

本文提出了一种基于深度学习回归体系结构的 3D 人体姿势结构预测方法，该方法借助于过度完备的自动编码器并考虑关节从属关系，优于现有技术，包括结构保留和预测准确性两方面。

使用深度神经网络进行 3D 人体姿态结构预测

Structured Prediction of 3D Human Pose with Deep Neural Networks

Current approaches to semantic image and scene understanding typically employ
rather simple object representations such as 2D or 3D bounding boxes. While
such coarse models are robust and allow for reliable object detection, they
discard much of the information about objects' 3D shape and pose, and thus do
not lend themselves well to higher-level reasoning. Here, we propose to base
scene understanding on a high-resolution object representation. An object class
- in our case cars - is modeled as a deformable 3D wireframe, which enables
fine-grained modeling at the level of individual vertices and faces. We augment
that model to explicitly include vertex-level occlusion, and embed all
instances in a common coordinate frame, in order to infer and exploit
object-object interactions. Specifically, from a single view we jointly
estimate the shapes and poses of multiple objects in a common 3D frame. A
ground plane in that frame is estimated by consensus among different objects,
which significantly stabilizes monocular 3D pose estimation. The fine-grained
model, in conjunction with the explicit 3D scene model, further allows one to
infer part-level occlusions between the modeled objects, as well as occlusions
by other, unmodeled scene elements. To demonstrate the benefits of such
detailed object class models in the context of scene understanding we
systematically evaluate our approach on the challenging KITTI street scene
dataset. The experiments show that the model's ability to utilize image
evidence at the level of individual parts improves monocular 3D pose estimation
w.r.t. both location and (continuous) viewpoint.

本文提出了一种基于 3D 可变形线框的高分辨率车辆对象表示，该表示能精细地建模单个点和面的水平，结合此表示和明确的 3D 场景模型，我们能够对场景进行更细致和准确的理解并从单个视角评估多个对象的位置与视点的单眼 3D 姿态估计。