ChainMo: Chain-Segment Motion Modeling across Arbitrary Skeleton Topologies

Generating motion for articulated characters with diverse skeleton topologies remains challenging because existing motion representations are often tightly coupled to the skeletal structures on which they are defined. Position-based representations are relatively flexible across skeletons, but remain sensitive to rig discretization, as the same articulated chain may contain different numbers of joints across rigs. Moreover, converting generated joint trajectories into animation-ready local rotations requires inverse kinematics, introducing ambiguities in under-constrained degrees of freedom such as bone twist. Directly modeling local rotations avoids this post-processing ambiguity, but further entangles motion generation with rig-specific kinematics and local rotation conventions. We address these limitations with **ChainMo**, a unified framework that separates shared motion modeling from target-rig-specific rotation realization. ChainMo represents motion using a dedicated root token and a variable set of disjoint, non-branching kinematic chain-segment tokens, forming a shared motion abstraction that is robust to variations in within-chain joint resolution. A target-rig-conditioned decoder realizes the abstract motion as coherent local joint rotations without relying on external inverse kinematics. For motion generation, we further introduce a flow-matching model with global spatiotemporal attention over the variable set of chain-segment tokens, enabling shared generative modeling across heterogeneous articulated characters. Extensive experiments on characters with diverse and previously unseen skeletons demonstrate strong generalization across kinematic structures and rig resolutions, accurate reconstruction of local joint rotations, and effective generative modeling in the learned chain-segment latent space. See our anonymous project page for more demos: https://anonymous1778.github.io/chainmo/.