论文标题
KP-RNN：人类运动预测和综合性能艺术的深度学习管道
KP-RNN: A Deep Learning Pipeline for Human Motion Prediction and Synthesis of Performance Art
论文作者
论文摘要
数字化综合人类运动是一个固有的复杂过程，它可以在虚拟现实等应用领域造成障碍。我们提供了一种预测人类运动的新方法KP-RNN，KP-RNN是一种神经网络，可以轻松地与现有的图像处理和发电管道集成。我们利用了一个新的人类运动数据集，带头，以及运动生成管道，现在的每个人舞蹈系统，以展示KP-RNN运动预测的有效性。我们发现，我们的神经网络可以有效地预测人类的舞蹈运动，这是使用铅数据集的未来作品的基线结果。由于KP-RNN可以与现在所有人跳舞的系统一起工作，因此我们认为我们的方法可以激发呈现人类头像动画的新方法。这项工作还通过利用可访问的神经网络来使数字平台中的性能艺术的可视化受益。
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.
