EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusion
MCML Authors
Cecilia Curreli
Florian Hofherr
Abhishek Saroha
Riccardo Marin
Prof. Dr.
Core PI
Daniel Cremers
Prof. Dr.
Director
Abstract
Cecilia Curreli
Florian Hofherr
Abhishek Saroha
Riccardo Marin
Prof. Dr.
Core PI
Daniel Cremers
Prof. Dr.
Director
Abstract
Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding the skeleton kinematics, severely limiting generalization, preventing cross-dataset training, and requiring complex data retargeting. We introduce EquiFusion, the first kinematics-agnostic model to solve this bottleneck, implementing a latent diffusion model with a permutation equivariant architecture. EquiFusion treats the kinematics' connectivity as an explicit input parameter, ensuring its internal computations are inherently agnostic to joint ordering and graph structure. This novel design enables truly cross-dataset generalization to unseen kinematics and unlocks novel zero-shot directions, such as motion prediction from partial or occluded observations and targeted limb generation. EquiFusion achieves state-of-the-art results on major benchmarks, being up to 75% more compact than previous kinematics-specific methods, while achieving faster training and inference. EquiFusion thus establishes a new, flexible standard for robust human motion prediction.
inproceedings CHM+26
ECCV 2026
19th European Conference on Computer Vision. Malmö, Sweden, Sep 08-12, 2026. To be published. Preprint available.
Authors
C. Curreli • F. Hofherr • D. Muhle • A. Saroha • R. Marin • D. CremersLinks
arXiv URLResearch Areas
BibTeXKey: CHM+26