When enough annotated training data is available, supervised deep-learning
algorithms excel at estimating human body pose and shape using a single camera.
The effects of too little such data being available can be mitigated by using
other information sources, such as databases of body shapes, to learn priors.
Unfortunately, such sources are not always available either. We show that, in
such cases, easy-to-obtain unannotated videos can be used instead to provide
the required supervisory signals. Given a trained model using too little
annotated data, we compute poses in consecutive frames along with the optical
flow between them. We then enforce consistency between the image optical flow
and the one that can be inferred from the change in pose from one frame to the
next. This provides enough additional supervision to effectively refine the
network weights and to perform on par with methods trained using far more
annotated data.

使用未标注视频作为监督信号，可以在没有足够的标注数据时提供额外的监督以优化网络权重，并与使用更多标注数据训练的方法相媲美。

使用动作线索监督低数据环境下的单帧身体姿势和形状估计

Using Motion Cues to Supervise Single-Frame Body Pose and Shape  Estimation in Low Data Regimes

To facilitate the analysis of human actions, interactions and emotions, we
compute a 3D model of human body pose, hand pose, and facial expression from a
single monocular image. To achieve this, we use thousands of 3D scans to train
a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with
fully articulated hands and an expressive face. Learning to regress the
parameters of SMPL-X directly from images is challenging without paired images
and 3D ground truth. Consequently, we follow the approach of SMPLify, which
estimates 2D features and then optimizes model parameters to fit the features.
We improve on SMPLify in several significant ways: (1) we detect 2D features
corresponding to the face, hands, and feet and fit the full SMPL-X model to
these; (2) we train a new neural network pose prior using a large MoCap
dataset; (3) we define a new interpenetration penalty that is both fast and
accurate; (4) we automatically detect gender and the appropriate body models
(male, female, or neutral); (5) our PyTorch implementation achieves a speedup
of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to
both controlled images and images in the wild. We evaluate 3D accuracy on a new
curated dataset comprising 100 images with pseudo ground-truth. This is a step
towards automatic expressive human capture from monocular RGB data. The models,
code, and data are available for research purposes at
this https URL

本研究提出了通过单目图像计算实现人体姿态、手部姿态和面部表情三维模型的方法，主要使用了一个新的、一致的、包含完全表达手和面部表情的人体模型 SMPL-X，可以对受控图像和自然环境图片进行三维模型匹配。

通过单张图片捕捉 3D 手、面部和身体表达

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and
prediction of human body pose in videos and motion capture. The ERD model is a
recurrent neural network that incorporates nonlinear encoder and decoder
networks before and after recurrent layers. We test instantiations of ERD
architectures in the tasks of motion capture (mocap) generation, body pose
labeling and body pose forecasting in videos. Our model handles mocap training
data across multiple subjects and activity domains, and synthesizes novel
motions while avoid drifting for long periods of time. For human pose labeling,
ERD outperforms a per frame body part detector by resolving left-right body
part confusions. For video pose forecasting, ERD predicts body joint
displacements across a temporal horizon of 400ms and outperforms a first order
motion model based on optical flow. ERDs extend previous Long Short Term Memory
(LSTM) models in the literature to jointly learn representations and their
dynamics. Our experiments show such representation learning is crucial for both
labeling and prediction in space-time. We find this is a distinguishing feature
between the spatio-temporal visual domain in comparison to 1D text, speech or
handwriting, where straightforward hard coded representations have shown
excellent results when directly combined with recurrent units.

提出了一种名为 ERD 模型的循环神经网络，用于识别和预测视频和动作捕捉中的人体姿势，在运动捕捉、人体姿态标记和视频姿态预测方面均表现优秀，并且代表了时空视觉领域的重要特点。