论文标题
部分可观测时空混沌系统的无模型预测
Style-ERD: Responsive and Coherent Online Motion Style Transfer
论文作者
论文摘要
运动样式转移是丰富角色动画的常见方法。运动样式转移算法通常是为在段中处理动作的离线设置而设计的。但是，对于在线动画应用程序（例如Motion Capture中的实时头像动画）等在线动画应用程序，需要将动作作为具有最小延迟的流进行处理。在这项工作中，我们为此设置实现了一种灵活的高质量运动样式转移方法。我们提出了一种新型的样式转移模型，即Style-ARD，以在线方式进行用编码器重新编码器结构进行风格化动作，以及一个结合起关注和暂时关注的新颖歧视器。我们的方法将运动风格化为具有统一模型的多种目标样式。尽管我们的方法针对在线设置，但它在运动现实主义和风格表现力方面的表现优于以前的离线方法，并在运行时效率方面带来了可观的提高
Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as realtime avatar animation from motion capture, motions need to be processed as a stream with minimal latency. In this work, we realize a flexible, high-quality motion style transfer method for this setting. We propose a novel style transfer model, Style-ERD, to stylize motions in an online manner with an Encoder-Recurrent-Decoder structure, along with a novel discriminator that combines feature attention and temporal attention. Our method stylizes motions into multiple target styles with a unified model. Although our method targets online settings, it outperforms previous offline methods in motion realism and style expressiveness and provides significant gains in runtime efficiency
