Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland.
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland.
Sensors (Basel). 2023 Feb 26;23(5):2597. doi: 10.3390/s23052597.
In the fourth industrial revolution, the scale of execution for interactive applications increased substantially. These interactive and animated applications are human-centric, and the representation of human motion is unavoidable, making the representation of human motions ubiquitous. Animators strive to computationally process human motion in a way that the motions appear realistic in animated applications. Motion style transfer is an attractive technique that is widely used to create realistic motions in near real-time. motion style transfer approach employs existing captured motion data to generate realistic samples automatically and updates the motion data accordingly. This approach eliminates the need for handcrafted motions from scratch for every frame. The popularity of deep learning (DL) algorithms reshapes motion style transfer approaches, as such algorithms can predict subsequent motion styles. The majority of motion style transfer approaches use different variants of deep neural networks (DNNs) to accomplish motion style transfer approaches. This paper provides a comprehensive comparative analysis of existing state-of-the-art DL-based motion style transfer approaches. The enabling technologies that facilitate motion style transfer approaches are briefly presented in this paper. When employing DL-based methods for motion style transfer, the selection of the training dataset plays a key role in the performance. By anticipating this vital aspect, this paper provides a detailed summary of existing well-known motion datasets. As an outcome of the extensive overview of the domain, this paper highlights the contemporary challenges faced by motion style transfer approaches.
在第四次工业革命中，交互式应用的执行规模大幅增加。这些交互式和动画应用以人为中心，不可避免地要表示人类运动，因此人类运动的表示无处不在。动画师努力以计算方式处理人类运动，使运动在动画应用中看起来逼真。运动风格迁移是一种很有吸引力的技术，广泛用于在接近实时的情况下生成逼真的运动。运动风格迁移方法利用现有的捕获运动数据自动生成逼真的样本，并相应地更新运动数据。这种方法消除了为每一帧从头开始手工制作运动的需要。深度学习（DL）算法的普及改变了运动风格迁移方法，因为这些算法可以预测后续的运动风格。大多数运动风格迁移方法使用不同的深度神经网络（DNN）变体来完成运动风格迁移方法。本文对现有的基于深度学习的运动风格迁移方法进行了全面的比较分析。本文简要介绍了促进运动风格迁移方法的使能技术。当使用基于深度学习的方法进行运动风格迁移时，训练数据集的选择对性能起着关键作用。为了预见这一重要方面，本文详细总结了现有的知名运动数据集。作为对该领域广泛概述的结果，本文突出了运动风格迁移方法面临的当代挑战。
