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: https://cyanzhao42.github.io/pose2motion

翻译：在计算机图形学中，为不同角色生成可信运动一直是长期目标。当前基于学习的运动合成方法依赖大规模运动数据集，而这些数据往往难以获取甚至无法获得。相比之下，姿态数据更易获取——静态角色姿态更易创建，且可通过计算机视觉领域最新进展从图像中直接提取。本文利用这一替代数据源，提出一种通过重定向实现神经运动合成的方法。该方法通过转移另一角色（可具有完全不同的骨骼结构）的现有运动捕捉数据集中的运动，为仅拥有姿态数据的角色生成合理运动。实验表明，该方法能有效融合源角色的运动特征与目标角色的姿态特征，在少量或含噪姿态数据集（从艺术家创作的若干姿态到直接从图像估计的含噪姿态）上均表现稳健。此外，用户调研显示，大多数参与者认为我们重定向后的运动更具观赏性、更逼真，且伪影更少。项目页面：https://cyanzhao42.github.io/pose2motion