We describe the first method to automatically estimate the 3D pose of the
human body as well as its 3D shape from a single unconstrained image. We
estimate a full 3D mesh and show that 2D joints alone carry a surprising amount
of information about body shape. The problem is challenging because of the
complexity of the human body, articulation, occlusion, clothing, lighting, and
the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a
recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D
body joint locations. We then fit (top-down) a recently published statistical
body shape model, called SMPL, to the 2D joints. We do so by minimizing an
objective function that penalizes the error between the projected 3D model
joints and detected 2D joints. Because SMPL captures correlations in human
shape across the population, we are able to robustly fit it to very little
data. We further leverage the 3D model to prevent solutions that cause
interpenetration. We evaluate our method, SMPLify, on the Leeds Sports,
HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect
to the state of the art.

本研究提出了一种基于 DeepCut 和 SMPL 模型的 3D 姿态估计方法，能够从单张无约束的图像中自动预测人体的姿态和形状，实现了对人体结构和运动的精确推断。

SMPL 约简：从单张图片自动估计三维人体姿态和形状

Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a  Single Image

In this paper, we introduce a new hierarchical model for human action
recognition using body joint locations. Our model can categorize complex
actions in videos, and perform spatio-temporal annotations of the atomic
actions that compose the complex action being performed.That is, for each
atomic action, the model generates temporal action annotations by estimating
its starting and ending times, as well as, spatial annotations by inferring the
human body parts that are involved in executing the action. our model includes
three key novel properties: (i) it can be trained with no spatial supervision,
as it can automatically discover active body parts from temporal action
annotations only; (ii) it jointly learns flexible representations for motion
poselets and actionlets that encode the visual variability of body parts and
atomic actions; (iii) a mechanism to discard idle or non-informative body parts
which increases its robustness to common pose estimation errors. We evaluate
the performance of our method using multiple action recognition benchmarks. Our
model consistently outperforms baselines and state-of-the-art action
recognition methods.

本文提出了一种基于人体关节点的动作识别的新层次模型，可以对视频中的复杂动作进行分类，并对组成复杂动作的原子动作进行时空注释。该模型具有自动发现主动体部的功能，联合学习动作提取和视觉可变性的灵活表示，以及去除空闲或无信息体部的机制等三个关键新颖性，在多个动作识别基准测试中表现优异。