A group animation generation method based on machine learning was proposed in order to reduce the complexity of generating mass of similar but different natural human motions in group animations. There are two models. Poses learning model was built based on Gaussian process latent variable model to characterize a specific motion and dynamic model was built in latent space to characterize the dynamic evolving process of neighboring poses in latent space. These models can be represented as probability distribution over all poses composing the motion by learning from existing motion data. Dynamic prediction can be made in latent space for giving initial state

then hundreds of latent trajectories by Hybrid Monte Carlo sampling according to given probability distribution can be obtained. Group animations can be implemented by generating a series of similar but different natural motions reconstructed from these latent trajectories

thereby avoid the difficulty and complexity of calculating geometric relationship and physical constrains in inverse kinematics.