Abstract : We present a novel, lossy compression method for human motion data that exploits both temporal and spatial coherence. We first build a compact skeleton pose model from a single motion using Principal Geodesic Analysis (PGA). The key idea is to perform compression by only storing the model parameters along with the end-joints and root joint trajectories in the output data. The input data are recovered by optimizing PGA variables to match end-effectors positions in an inverse kinematics approach. Our experimental results show that considerable compression rates can be obtained using our method, with few reconstruction and perceptual errors. Thanks to the embedding of the pose model, our system can also be suitable for motion editing purposes.