As virtual reality (VR) applications become popular, the desire to enable high-quality, lightweight, and mobile VR can potentially be achieved by performing the VR rendering and encoding computations at the edge and streaming the rendered video to the VR glasses. However, if the rendering has to be performed after the edge gets to know of the user’s new head and body position, the ultra-low latency requirements of VR will not be met by the roundtrip delay. In this article, we introduce edge intelligence, wherein the edge can predict, pre-render and cache the VR video in advance, to be streamed to the user VR glasses as soon as needed. The edge-based predictive pre-rendering approach can address the challenging six Degrees of Freedom (6DoF) VR content. Compared to 360-degree videos and 3DoF (head motion only) VR, 6DoF VR supports both head and body motion, thus not only viewing direction but also viewing position can change. Hence, our proposed VR edge intelligence comprises of predicting both the head and body motions of a user accurately using past head and body motion traces. In this article, we develop a multi-task long short-term memory (LSTM) model for body motion prediction and a multi-layer perceptron (MLP) model for head motion prediction. We implement the deep learning-based motion prediction models and validate their accuracy and effectiveness using a dataset of over 840,000 samples for head and body motion.

中文翻译：

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边缘的运动预测和预渲染可实现超低延迟的移动6DoF体验

随着虚拟现实（VR）应用程序的普及，可以通过在边缘执行VR渲染和编码计算并将渲染的视频流式传输到VR眼镜，来实现实现高质量，轻量级和移动VR的愿望。但是，如果必须在边缘了解用户的新头部和身体位置之后执行渲染，则往返延迟将无法满足VR的超低延迟要求。在本文中，我们介绍了边缘智能，其中边缘可以预先预测，预渲染和缓存VR视频，并在需要时将其流式传输到用户VR眼镜。基于边缘的预测性预渲染方法可以解决具有挑战性的六个自由度（6DoF）VR内容。与360度视频和3DoF（仅头部运动）VR相比，6DoF VR支持头部和身体运动，因此不仅观看方向而且观看位置都可以改变。因此，我们提出的VR边缘智能包括使用过去的头部和身体运动轨迹准确预测用户的头部和身体运动。在本文中，我们开发了用于身体运动预测的多任务长期短期记忆（LSTM）模型和用于头部运动预测的多层感知器（MLP）模型。我们实施基于深度学习的运动预测模型，并使用针对头部和身体运动的超过840,000个样本的数据集来验证其准确性和有效性。在本文中，我们开发了用于身体运动预测的多任务长期短期记忆（LSTM）模型和用于头部运动预测的多层感知器（MLP）模型。我们实施基于深度学习的运动预测模型，并使用针对头部和身体运动的超过840,000个样本的数据集来验证其准确性和有效性。在本文中，我们开发了用于身体运动预测的多任务长期短期记忆（LSTM）模型和用于头部运动预测的多层感知器（MLP）模型。我们实施基于深度学习的运动预测模型，并使用针对头部和身体运动的超过840,000个样本的数据集来验证其准确性和有效性。