# Homogeneous Dynamics Space for Heterogeneous Humans

arXiv: https://arxiv.org/abs/2412.06146 · HTML: https://mlsanity.com/paper/2412.06146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 85 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 28 of 28)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [BABEL: Bodies, Action and Behavior with English Labels](https://mlsanity.com/paper/2106.09696/) (2021)
- [Perpetual Humanoid Control for Real-time Simulated Avatars](https://mlsanity.com/paper/2305.06456/) (2023)
- [Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors](https://mlsanity.com/paper/2203.08528/) (2022)
- [Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation](https://mlsanity.com/paper/2106.05969/) (2021)
- [Detailed 2D-3D Joint Representation for Human-Object Interaction](https://mlsanity.com/paper/2004.08154/) (2020)
- [Ego-Pose Estimation and Forecasting as Real-Time PD Control](https://mlsanity.com/paper/1906.03173/) (2019)
- [PaStaNet: Toward Human Activity Knowledge Engine](https://mlsanity.com/paper/2004.00945/) (2020)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [Differentiable Dynamics for Articulated 3d Human Motion Reconstruction](https://mlsanity.com/paper/2205.12256/) (2022)
- [Trajectory Optimization for Physics-Based Reconstruction of 3d Human Pose from Monocular Video](https://mlsanity.com/paper/2205.12292/) (2022)
- [Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture](https://mlsanity.com/paper/2203.14065/) (2022)
- [HAKE: A Knowledge Engine Foundation for Human Activity Understanding](https://mlsanity.com/paper/2202.06851/) (2022)
- [Revisit Human-Scene Interaction via Space Occupancy](https://mlsanity.com/paper/2312.02700/) (2023)
- [From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding](https://mlsanity.com/paper/2304.00553/) (2023)
- [SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders](https://mlsanity.com/paper/2407.13460/) (2024)
- [Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation](https://mlsanity.com/paper/2312.05473/) (2023)
- [Weakly-supervised Learning of Human Dynamics](https://mlsanity.com/paper/2007.08969/) (2020)
- [GroundLink: A Dataset Unifying Human Body Movement and Ground Reaction Dynamics](https://mlsanity.com/paper/2310.03930/) (2023)
- [AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale](https://mlsanity.com/paper/2406.18537/) (2024)
- [ImDy: Human Inverse Dynamics from Imitated Observations](https://mlsanity.com/paper/2410.17610/) (2024)
- [Towards Activated Muscle Group Estimation in the Wild](https://mlsanity.com/paper/2303.00952/) (2023)
- [Evaluating Muscle Synergies with EMG Data and Physics Simulation in the Neurorobotics Platform](https://mlsanity.com/paper/2201.05496/) (2022)
- [On-line feasible wrench polytope evaluation based on human musculoskeletal models: an iterative convex hull method](https://mlsanity.com/paper/2110.06790/) (2021)

## Built on by (most cited first, 1 of 1)

- [Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface](https://mlsanity.com/paper/2604.24415/) (2026)

## Abstract (arXiv)

Analyses of human motion kinematics have achieved tremendous advances. However, the production mechanism, known as human dynamics, is still undercovered. In this paper, we aim to push data-driven human dynamics understanding forward. We identify a major obstacle to this as the heterogeneity of existing human motion understanding efforts. Specifically, heterogeneity exists in not only the diverse kinematics representations and hierarchical dynamics representations but also in the data from different domains, namely biomechanics and reinforcement learning. With an in-depth analysis of the existing heterogeneity, we propose to emphasize the beneath homogeneity: all of them represent the homogeneous fact of human motion, though from different perspectives. Given this, we propose Homogeneous Dynamics Space (HDyS) as a fundamental space for human dynamics by aggregating heterogeneous data and training a homogeneous latent space with inspiration from the inverse-forward dynamics procedure. Leveraging the heterogeneous representations and datasets, HDyS achieves decent mapping between human kinematics and dynamics. We demonstrate the feasibility of HDyS with extensive experiments and applications. The project page is https://foruck.github.io/HDyS.