Digitally synthesizing human motion is an inherently complex process, which can create obstacles in application areas such as virtual reality. We offer a new approach for predicting human motion, KP-RNN, a neural network which can integrate easily with existing image processing and generation pipelines. We utilize a new human motion dataset of performance art, Take The Lead, as well as the motion generation pipeline, the Everybody Dance Now system, to demonstrate the effectiveness of KP-RNN's motion predictions. We have found that our neural network can predict human dance movements effectively, which serves as a baseline result for future works using the Take The Lead dataset. Since KP-RNN can work alongside a system such as Everybody Dance Now, we argue that our approach could inspire new methods for rendering human avatar animation. This work also serves to benefit the visualization of performance art in digital platforms by utilizing accessible neural networks.

翻译：数字合成人体运动是一个天然复杂的过程，这可能在虚拟现实等应用领域造成障碍。我们提出了一种新的人体运动预测方法——KP-RNN，该神经网络能够与现有图像处理和生成流水线轻松集成。我们利用了新的表演艺术人体运动数据集Take The Lead，以及运动生成流水线Everybody Dance Now系统，来验证KP-RNN运动预测的有效性。研究发现，该神经网络能够有效预测人类舞蹈动作，这为未来使用Take The Lead数据集的研究提供了基准结果。由于KP-RNN能够与Everybody Dance Now等系统协同工作，我们认为该方法可启发渲染虚拟人物动画的新途径。本工作还通过利用易获取的神经网络，为数字化平台上表演艺术的可视化呈现提供了助益。