We present a probabilistic framework to generate character animations based
on weak control signals, such that the synthesized motions are realistic while
retaining the stochastic nature of human movement. The proposed architecture,
which is designed as a hierarchical recurrent model, maps each sub-sequence of
motions into a stochastic latent code using a variational autoencoder extended
over the temporal domain. We also propose an objective function which respects
the impact of each joint on the pose and compares the joint angles based on
angular distance. We use two novel quantitative protocols and human qualitative
assessment to demonstrate the ability of our model to generate convincing and
diverse periodic and non-periodic motion sequences without the need for strong
control signals.

提出了一种基于概率框架生成角色动画的方法，该方法基于弱控制信号生成逼真的动作序列，并保留人类运动的随机性。所提出的层次循环模型映射每个子序列的运动到一个随机潜变量代码，使用在时间领域上扩展的变分自动编码器。同时，提出一个考虑每个关节对姿势产生影响的目标函数，并基于角度距离比较关节角度。打造两个新的定量协议和人类定性评估，证明算法能够生成具有说服力和多样性的周期性和非周期性运动序列，而不需要强控制信号。