Anytime 3D human pose forecasting is crucial to synchronous real-world
human-machine interaction, where the term ``anytime" corresponds to predicting
human pose at any real-valued time step. However, to the best of our knowledge,
all the existing methods in human pose forecasting perform predictions at
preset, discrete time intervals. Therefore, we introduce AnyPose, a lightweight
continuous-time neural architecture that models human behavior dynamics with
neural ordinary differential equations. We validate our framework on the
Human3.6M, AMASS, and 3DPW dataset and conduct a series of comprehensive
analyses towards comparison with existing methods and the intersection of human
pose and neural ordinary differential equations. Our results demonstrate that
AnyPose exhibits high-performance accuracy in predicting future poses and takes
significantly lower computational time than traditional methods in solving
anytime prediction tasks.

AnyPose 是一个轻量级的连续时间神经架构，使用神经常微分方程模拟人类行为动态，并在 Human3.6M、AMASS 和 3DPW 数据集上验证了其在预测未来姿势方面的高准确性和较低的计算时间。

AnyPose：基于神经常微分方程的任意时间三维人体姿势预测

AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary  Differential Equations

The task of three-dimensional (3D) human pose estimation from a single image
can be divided into two parts: (1) Two-dimensional (2D) human joint detection
from the image and (2) estimating a 3D pose from the 2D joints. Herein, we
focus on the second part, i.e., a 3D pose estimation from 2D joint locations.
The problem with existing methods is that they require either (1) a 3D pose
dataset or (2) 2D joint locations in consecutive frames taken from a video
sequence. We aim to solve these problems. For the first time, we propose a
method that learns a 3D human pose without any 3D datasets. Our method can
predict a 3D pose from 2D joint locations in a single image. Our system is
based on the generative adversarial networks, and the networks are trained in
an unsupervised manner. Our primary idea is that, if the network can predict a
3D human pose correctly, the 3D pose that is projected onto a 2D plane should
not collapse even if it is rotated perpendicularly. We evaluated the
performance of our method using Human3.6M and the MPII dataset and showed that
our network can predict a 3D pose well even if the 3D dataset is not available
during training.

本文提出了一种使用生成对抗网络进行无监督学习的方法，能够从单张图片的 2D 关节点位置预测出 3D 人体姿态，无需使用 3D 数据集，而且即使在训练时缺乏数据，该方法也能很好地预测 3D 姿态。