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.

中文翻译：

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使用分层深度潜变量模型的概率特征运动合成

我们提出了一个基于弱控制信号生成角色动画的概率框架，这样合成的动作是真实的，同时保留了人类运动的随机性。所提出的架构被设计为分层循环模型，使用在时间域上扩展的变分自动编码器将每个运动子序列映射到随机潜在代码中。我们还提出了一个目标函数，该函数尊重每个关节对姿势的影响，并根据角距离比较关节角度。我们使用两种新颖的定量协议和人类定性评估来证明我们的模型能够在不需要强控制信号的情况下生成令人信服的多样化周期性和非周期性运动序列。