Variational Conditioning of Deep Recurrent Networks for Modeling Complex Motion Dynamics

Abstract

This work introduces stochastic models to address the problem of complex motion generation. Long-term motion generation is the primary task in several fields; however, less work has happened in this direction and is restricted to single-person activities. Looking forward, this work strives to solve the problem of two-person 3D motion generation. Single-person motion generation is comparatively simpler than two-person, where the complexity is significantly higher. Error-propagation during motion generation is a key challenge. Current approaches often fail to keep the predicted skeletons on the manifold of valid poses. Another challenge is when the model restricts its learning to the training distribution. This leads to stagnation in generated moves. To this end, an end-to-end hierarchical auto-regressive model is proposed for efficient long-term motion generation. It is further constrained by an alignment network to reduce prediction errors. Proposed approach shows comparable results on prediction tasks, while outperforming the state-of-the-art on long-term motion generation.

Keywords

• Recurrent neural networks
• 3D motion prediction
• pose embedding