# DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics

arXiv: https://arxiv.org/abs/2309.13742 · HTML: https://mlsanity.com/paper/2309.13742/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [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)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://mlsanity.com/paper/1804.02717/) (2018)
- [Learning to Generate Diverse Dance Motions with Transformer](https://mlsanity.com/paper/2008.08171/) (2020)
- [ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters](https://mlsanity.com/paper/2210.06063/) (2022)
- [GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction](https://mlsanity.com/paper/2110.00380/) (2021)
- [A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control](https://mlsanity.com/paper/2105.10066/) (2021)

## Built on by (most cited first, 10 of 10)

- [TLControl: Trajectory and Language Control for Human Motion Synthesis](https://mlsanity.com/paper/2311.17135/) (2023)
- [EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation](https://mlsanity.com/paper/2312.02256/) (2023)
- [StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework](https://mlsanity.com/paper/2405.05691/) (2024)
- [PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers](https://mlsanity.com/paper/2505.04002/) (2025)
- [ChatHuman: Chatting about 3D Humans with Tools](https://mlsanity.com/paper/2405.04533/) (2024)
- [AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale](https://mlsanity.com/paper/2406.18537/) (2024)
- [Text2Interact: High-Fidelity and Diverse Text-to-Two-Person Interaction Generation](https://mlsanity.com/paper/2510.06504/) (2025)
- [MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning](https://mlsanity.com/paper/2411.16964/) (2024)
- [PhysHMR: Learning Humanoid Control Policies from Vision for Physically Plausible Human Motion Reconstruction](https://mlsanity.com/paper/2510.02566/) (2025)
- [Scalable Group Choreography via Variational Phase Manifold Learning](https://mlsanity.com/paper/2407.18839/) (2024)

## Abstract (arXiv)

Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports, and healthcare, for motion prediction and data augmentation. Recent kinematics-based generative motion models offer impressive scalability in modeling extensive motion data, albeit without an interface to reason about and interact with physics. While simulator-in-the-loop learning approaches enable highly physically realistic behaviors, the challenges in training often affect scalability and adoption. We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics. DROP can be viewed as a highly stable, minimalist physics-based human simulator that interfaces with a kinematics-based generative motion prior. Utilizing projective dynamics, DROP allows flexible and simple integration of the learned motion prior as one of the projective energies, seamlessly incorporating control provided by the motion prior with Newtonian dynamics. Serving as a model-agnostic plug-in, DROP enables us to fully leverage recent advances in generative motion models for physics-based motion synthesis. We conduct extensive evaluations of our model across different motion tasks and various physical perturbations, demonstrating the scalability and diversity of responses.