论文标题
使用分层深度可变模型合成概率角色运动综合
Probabilistic Character Motion Synthesis using a Hierarchical Deep Latent Variable Model
论文作者
论文摘要
我们提出了一个概率框架，以基于弱控制信号生成角色动画，以便合成动作是现实的，同时保留了人类运动的随机性。所提出的体系结构被设计为层次复发模型，将运动的每个子序列映射到随机潜在代码中，使用延伸于时间域上的变异自动编码器。我们还提出了一个目标函数，该目标函数尊重每个关节对姿势的影响，并根据角度距离进行比较关节角度。我们使用两种新颖的定量方案和人类定性评估来证明我们的模型产生令人信服和多样化的周期性和非周期性运动序列而无需强大的控制信号。
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.
