PhysIKDance: Contact- and Slide-Aware Dual-Space Music-to-Dance Generation via Probabilistic Inverse Kinematics

Audio-driven 3D dance generation is essential for virtual avatars and immersive digital experiences. Despite recent progress, methods that directly predict joint rotations often accumulate errors along the kinematic chain, leading to foot skating, ground penetration and inconsistent support. To tackle the issue, we propose PhysIKDance, a simple yet effective dual-space framework decouples physical reasoning from rotation reconstruction, integrating physically supervised geometric generation with music-conditioned rotation recovery for high-fidelity dance motion generation. In Joint Coordinate Space, a diffusion model jointly predicts root translation, joint coordinates, axial twist, and foot-interaction states. A state-aware objective couples root and local-foot motion to suppress drift during planted support while preserving intentional sliding, complemented by constraints on support height and ground penetration. In Joint Rotation Space, a music-conditioned, two-stage invertible inverse-kinematics module recovers joint swing and combines it with axial twist to produce SMPL-compatible rotations. Cross-space consistency supervision further connects geometric plausibility with accurate rotation reconstruction. Extensive experiments on AIST++ and FineDance demonstrate that PhysIKDance substantially improves foot–ground consistency and overall physical plausibility while achieving competitive motion quality, musical alignment, and motion diversity. Code will be released upon acceptance.