The complex spatial-temporal structure within human motion capture data often makes it extremely challenging to adapt the motion generation fields, ranging from data-driven character animation, sequence splic-ing and motion style fusion. Inspired by the popularity of deep learning theories in computer graphics, four kinds of deep generative models (i.e., restricted Boltzmann machine, recurrent neural network, convolutional neural network, deep reinforcement learning) and their hybrid models, are comprehensively surveyed for human skeletal motion generation, including studying their topology structures and theoretical optimizations. Meanwhile, we in-vestigate the superiorities of these representative deep learning models in extracting the spatial-temporal motion features, and quantitatively compare their performances in different motion generation tasks. Finally, we carefully survey their potential challenges in complex motion generation, and discuss the future trends of recent deep learning models on robust motion generations.%人体运动捕捉中复杂的时空结构信息使其在数据驱动角色动画、序列拼接和风格融合等运动生成研究领域极具挑战性. 聚焦于深度学习在计算机图形学中所获得的巨大成功, 首先从模型结构和理论优化2个方面对运动捕捉数据中人体骨骼动画生成所使用的4类生成式深度学习模型(受限玻尔兹曼机、循环神经网络、卷积神经网络、深度强化学习)及其混合学习方法进行全面概括和总结; 接着探讨这些典型深度学习模型对人体骨骼运动数据时空特征提取的能力, 并量化对比其在不同生成任务中的实验效果; 最后深入分析各种深度模型的优势及亟待解决的难题,并对新型深度学习模型在运动生成中的发展趋势进行了展望.
展开▼
