Bi-modal Prediction and Transformation Coding for Compressing Complex Human Dynamics

For dynamic human motion sequences, the original KeyNode-Driven codec often struggles to retain compression efficiency when confronted with rapid movements or strong non-rigid deformations. This paper proposes a novel Bi-modal coding framework that enhances the flexibility of motion representation by integrating semantic segmentation and region-specific transformation modeling. The rigid transformation model (rotation & translation) is extended with a hybrid scheme that selectively applies affine transformations-rotation, translation, scaling, and shearing-only to deformation-rich regions (e.g., the torso, where loose clothing induces high variability), while retaining rigid models elsewhere. The affine model is decomposed into minimal parameter sets for efficient coding and combined through a component selection strategy guided by a Lagrangian Rate-Distortion optimization. The results show that the Bi-modal method achieves more accurate mesh deformation, especially in sequences involving complex non-rigid motion, without compromising compression efficiency in simpler regions, with an average bit-rate saving of 33.81% compared to the baseline.

Huong Hoang, Keito Suzuki, Truong Nguyen, Pamela Cosman · 2025