# A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting arXiv: https://arxiv.org/abs/2509.15443 ยท HTML: https://mlsanity.com/paper/2509.15443/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Real-world robot offline RL, one of 13 directions in Offline RL and imitation learning from data. - Builds on 36 of its 64 references that are in the map. - First posted on arXiv 18 Sep 2025. ## Placed in - Offline RL and imitation learning from data / [Real-world robot offline RL](https://mlsanity.com/offlinerl/real-world-robot-offline-rl/) ## Builds on (most cited first, 30 of 36) - [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019) - [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018) - [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022) - [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022) - [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023) - [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022) - [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022) - [MoMask: Generative Masked Modeling of 3D Human Motions](https://mlsanity.com/paper/2312.00063/) (2023) - [The KIT Motion-Language Dataset](https://mlsanity.com/paper/1607.03827/) (2016) - [BABEL: Bodies, Action and Behavior with English Labels](https://mlsanity.com/paper/2106.09696/) (2021) - [BEHAVE: Dataset and Method for Tracking Human Object Interactions](https://mlsanity.com/paper/2204.06950/) (2022) - [Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset](https://mlsanity.com/paper/2307.00818/) (2023) - [Perpetual Humanoid Control for Real-time Simulated Avatars](https://mlsanity.com/paper/2305.06456/) (2023) - [WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion](https://mlsanity.com/paper/2312.07531/) (2023) - [OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning](https://mlsanity.com/paper/2406.08858/) (2024) - [HumanPlus: Humanoid Shadowing and Imitation from Humans](https://mlsanity.com/paper/2406.10454/) (2024) - [HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes](https://mlsanity.com/paper/2210.09729/) (2022) - [Expressive Whole-Body Control for Humanoid Robots](https://mlsanity.com/paper/2402.16796/) (2024) - [World-Grounded Human Motion Recovery via Gravity-View Coordinates](https://mlsanity.com/paper/2409.06662/) (2024) - [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025) - [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020) - [TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos](https://mlsanity.com/paper/2403.17346/) (2024) - [TWIST: Teleoperated Whole-Body Imitation System](https://mlsanity.com/paper/2505.02833/) (2025) - [Neural Kinematic Networks for Unsupervised Motion Retargetting](https://mlsanity.com/paper/1804.05653/) (2018) - [GMT: General Motion Tracking for Humanoid Whole-Body Control](https://mlsanity.com/paper/2506.14770/) (2025) - [Visual Imitation Enables Contextual Humanoid Control](https://mlsanity.com/paper/2505.03729/) (2025) - [KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills](https://mlsanity.com/paper/2506.12851/) (2025) - [UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robots](https://mlsanity.com/paper/2507.07356/) (2025) - [Reconstructing People, Places, and Cameras](https://mlsanity.com/paper/2412.17806/) (2024) - [Scaling Large Motion Models with Million-Level Human Motions](https://mlsanity.com/paper/2410.03311/) (2024) ## Built on by (most cited first, 2 of 2) - [GenTrack: Physical Alignment for Robot-Native Motion Generation and Zero-Shot Humanoid Tracking](https://mlsanity.com/paper/2608.01410/) (2026) - [Active Stereo-Camera Outperforms Multi-Sensor Setup in ACT Imitation Learning for Humanoid Manipulation](https://mlsanity.com/paper/2603.28422/) (2026) ## Abstract (arXiv) Human-to-humanoid imitation learning presents a promising pathway to address the severe data scarcity bottleneck in robotics by utilizing abundant, large-scale human motion collections. However, scaling this paradigm requires addressing two key challenges. First, human motion data acquired from videos, motion capture systems, or generative models often contains spatial noise, jitter, and frame-level flickering, which can be amplified during retargeting and lead to unsafe or physically infeasible robot motions. Second, existing motion retargeting methods typically rely on frame-by-frame numerical optimization, making them too computationally expensive for large-scale dataset synthesis. To overcome these limitations, we introduce Implicit Kinodynamic Motion Retargeting (IKMR), a highly scalable, neural-based data transformation pipeline. IKMR leverages a skeleton-based graph convolutional dual autoencoder to map cross-structural human and humanoid kinematic configurations into a shared topological latent space. To guarantee the physical viability of the generated data, the framework incorporates a physics-informed refinement phase that utilizes simulated physical tracking feedback to learn a robust motion prior. This implicit formulation fundamentally resolves both challenges. By shifting the computational burden from online optimization to offline inference, IKMR achieves an unprecedented data conversion throughput exceeding 5000 frames per second. Furthermore, leveraging the learned motion prior, it functions as an intrinsic data curation mechanism and naturally filters out high-frequency noise and spatial jitters from source data, yielding smooth trajectories that ensure physical hardware safety. Extensive evaluations, including real-world whole-body control deployments on humanoid robot, confirm that IKMR bridges the gap between human motion and robotic data.