Motion Compression using Principal Geodesics Analysis

Maxime Tournier (1) , Xiaomao Wu (1) , Nicolas Courty (2) , Élise Arnaud (3) , Lionel Reveret (1)

1 EVASION - Virtual environments for animation and image synthesis of natural objects
2 VALORIA - Laboratoire de Recherche en Informatique et ses Applications de Vannes et Lorient
3 PERCEPTION - Interpretation and Modelling of Images and Videos

Due to the growing need for large quantities of human animation data in the entertainment industry, it has became a necessity to compress motion capture sequences in order to ease their storage and transmission. 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 Geodesics 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.

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]

Maxime Tournier, Xiaomao Wu, Nicolas Courty, Élise Arnaud, Lionel Reveret. Motion Compression using Principal Geodesics Analysis. [Research Report] RR-6648, INRIA. 2008, pp.24. ⟨inria-00321983⟩