Creating believable motions for various characters has long been a goal in
computer graphics. Current learning-based motion synthesis methods depend on
extensive motion datasets, which are often challenging, if not impossible, to
obtain. On the other hand, pose data is more accessible, since static posed
characters are easier to create and can even be extracted from images using
recent advancements in computer vision. In this paper, we utilize this
alternative data source and introduce a neural motion synthesis approach
through retargeting. Our method generates plausible motions for characters that
have only pose data by transferring motion from an existing motion capture
dataset of another character, which can have drastically different skeletons.
Our experiments show that our method effectively combines the motion features
of the source character with the pose features of the target character, and
performs robustly with small or noisy pose data sets, ranging from a few
artist-created poses to noisy poses estimated directly from images.
Additionally, a conducted user study indicated that a majority of participants
found our retargeted motion to be more enjoyable to watch, more lifelike in
appearance, and exhibiting fewer artifacts. Project page:
this https URL

通过姿势数据和神经运动合成方法，我们利用现有的运动捕捉数据集将合理的动作转移到只有姿势数据的角色上，实验证明该方法可以同时有效地结合源角色的运动特征和目标角色的姿势特征，并通过小型或嘈杂的姿势数据集生成逼真的动作，被参与者评为更加享受观看、外观更逼真且表现更加平滑。

姿态至动作：带有姿态先验的跨域动作重定位

Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior

Managing and analyzing pose data is a complex task, with challenges ranging
from handling diverse file structures and data types to facilitating effective
data manipulations such as normalization and augmentation. This paper presents
\texttt{pose-format}, a comprehensive toolkit designed to address these
challenges by providing a unified, flexible, and easy-to-use interface. The
library includes a specialized file format that encapsulates various types of
pose data, accommodating multiple individuals and an indefinite number of time
frames, thus proving its utility for both image and video data. Furthermore, it
offers seamless integration with popular numerical libraries such as NumPy,
PyTorch, and TensorFlow, thereby enabling robust machine-learning applications.
Through benchmarking, we demonstrate that our \texttt{.pose} file format offers
vastly superior performance against prevalent formats like OpenPose, with added
advantages like self-contained pose specification. Additionally, the library
includes features for data normalization, augmentation, and easy-to-use
visualization capabilities, both in Python and Browser environments.
\texttt{pose-format} emerges as a one-stop solution, streamlining the
complexities of pose data management and analysis.

这篇论文介绍了 pose-format，它是一个综合工具包，通过提供统一、灵活和易于使用的接口来解决姿势数据管理和分析的复杂性。pose-format 具有专门的文件格式，可以封装多种类型的姿势数据，适用于图像和视频数据。该工具包还与 NumPy、PyTorch 和 TensorFlow 等流行的数值库无缝集成，以实现强大的机器学习应用。通过基准测试，证明了.pose 文件格式相对于 OpenPose 等普遍格式具有极高的性能，并具有自包含的姿势规范等优点。此外，该工具包还包括数据归一化、增强和易于使用的可视化功能，适用于 Python 和浏览器环境。pose-format 成为简化姿势数据管理和分析的一站式解决方案。