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

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