NeCR: Neural Contact-Consistent Retargeting for Whole-Body Humanoid Tracking

Retargeting human motion to a humanoid becomes particularly challenging for multi-contact motions, in which the knees, shins, and forearms support the body alongside the feet. Crawling, kneeling, and getting up from the floor depend on such whole-body contacts, and a robot with different proportions may be unable to reproduce contacts as the source human made them. Learning-based retargeters preserve motion semantics but cannot guarantee physical validity at inference, so a contact may be reproduced while penetrating the ground or sliding. Optimization- based retargeters enforce physical constraints, either frame by frame, where they can misplace support, or over the whole sequence, at a cost that grows with its length. To address this challenge, we present NeCR, a hybrid neural–optimization framework in which a learned contact model specifies where, when, and how strongly the optimizer corrects the motion. A neural retargeter first produces a semantically faithful nominal trajectory. Then a temporal model predicts continuous whole-body contact probabilities for the optimizer, which adjusts only the degrees of freedom of regions in robot-feasible contact. Therefore, the resulting problem is sparse and block-banded and can be solved in three Gauss–Newton sweeps. Evaluated on public human motion datasets retargeted to a Unitree G1, NeCR removes penetration and cuts support-phase sliding from 20.6% to 4.4% at a cost of only 0.16 cm MPKPE, and runs at 5.9 ms per frame. Policies trained on its references track more reliably on two independently implemented trackers and transfer zero-shot to a real G1 for crawling and fall recovery. Code, retargeted datasets, and trained policies will be released.