# QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors

arXiv: https://arxiv.org/abs/2306.05666 · HTML: https://mlsanity.com/paper/2306.05666/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://mlsanity.com/paper/1804.02717/) (2018)
- [Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors](https://mlsanity.com/paper/2203.08528/) (2022)
- [Preparing for the Unknown: Learning a Universal Policy with Online System Identification](https://mlsanity.com/paper/1702.02453/) (2017)
- [Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors](https://mlsanity.com/paper/2103.17265/) (2021)
- [SimPoE: Simulated Character Control for 3D Human Pose Estimation](https://mlsanity.com/paper/2104.00683/) (2021)
- [AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing](https://mlsanity.com/paper/2207.13784/) (2022)
- [Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs](https://mlsanity.com/paper/1703.08014/) (2017)
- [Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation](https://mlsanity.com/paper/2106.05969/) (2021)
- [GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning](https://mlsanity.com/paper/1810.05762/) (2018)
- [Ego-Pose Estimation and Forecasting as Real-Time PD Control](https://mlsanity.com/paper/1906.03173/) (2019)
- [Learning human behaviors from motion capture by adversarial imitation](https://mlsanity.com/paper/1707.02201/) (2017)
- [QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars](https://mlsanity.com/paper/2209.09391/) (2022)
- [Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis](https://mlsanity.com/paper/2006.07364/) (2020)
- [Embodied Scene-aware Human Pose Estimation](https://mlsanity.com/paper/2206.09106/) (2022)
- [TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors](https://mlsanity.com/paper/2105.04605/) (2021)
- [Neural3Points: Learning to Generate Physically Realistic Full-body Motion for Virtual Reality Users](https://mlsanity.com/paper/2209.05753/) (2022)
- [Combining Motion Matching and Orientation Prediction to Animate Avatars for Consumer-Grade VR Devices](https://mlsanity.com/paper/2209.11478/) (2022)

## Built on by (most cited first, 28 of 28)

- [OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning](https://mlsanity.com/paper/2406.08858/) (2024)
- [MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting](https://mlsanity.com/paper/2409.14393/) (2024)
- [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)
- [EgoPoser: Robust Real-Time Egocentric Pose Estimation from Sparse and Intermittent Observations Everywhere](https://mlsanity.com/paper/2308.06493/) (2023)
- [Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Camera](https://mlsanity.com/paper/2401.00847/) (2024)
- [HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations](https://mlsanity.com/paper/2403.03561/) (2024)
- [MultiPhys: Multi-Person Physics-aware 3D Motion Estimation](https://mlsanity.com/paper/2404.11987/) (2024)
- [Real-Time Simulated Avatar from Head-Mounted Sensors](https://mlsanity.com/paper/2403.06862/) (2024)
- [Ego4o: Egocentric Human Motion Capture and Understanding from Multi-Modal Input](https://mlsanity.com/paper/2504.08449/) (2025)
- [DivaTrack: Diverse Bodies and Motions from Acceleration-Enhanced Three-Point Trackers](https://mlsanity.com/paper/2402.09211/) (2024)
- [EnvPoser: Environment-aware Realistic Human Motion Estimation from Sparse Observations with Uncertainty Modeling](https://mlsanity.com/paper/2412.10235/) (2024)
- [EventEgo3D++: 3D Human Motion Capture from a Head-Mounted Event Camera](https://mlsanity.com/paper/2502.07869/) (2025)
- [UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units](https://mlsanity.com/paper/2505.09393/) (2025)
- [Self-Avatar Animation in Virtual Reality: Impact of Motion Signals Artifacts on the Full-Body Pose Reconstruction](https://mlsanity.com/paper/2404.18628/) (2024)
- [DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization](https://mlsanity.com/paper/2406.14567/) (2024)
- [Learning Smooth Time-Varying Linear Policies with an Action Jacobian Penalty](https://mlsanity.com/paper/2602.18312/) (2026)
- [Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors](https://mlsanity.com/paper/2603.16233/) (2026)
- [PlaMo: Plan and Move in Rich 3D Physical Environments](https://mlsanity.com/paper/2406.18237/) (2024)
- [Shape-aware Inertial Poser: Motion Tracking for Humans with Diverse Shapes Using Sparse Inertial Sensors](https://mlsanity.com/paper/2510.17101/) (2025)
- [DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation](https://mlsanity.com/paper/2604.20841/) (2026)
- [EgoExoMoCap: Distributed Ego-Exo Human Motion Capture](https://mlsanity.com/paper/2607.15868/) (2026)
- [LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality](https://mlsanity.com/paper/2410.06437/) (2024)
- [Mem-MLP: Real-Time 3D Human Motion Generation from Sparse Inputs](https://mlsanity.com/paper/2511.16264/) (2025)
- [Dancing Points: Synthesizing Ballroom Dancing with Three-Point Inputs](https://mlsanity.com/paper/2601.02096/) (2026)
- [EgoPoseVR: Spatiotemporal Multi-Modal Reasoning for Egocentric Full-Body Pose in Virtual Reality](https://mlsanity.com/paper/2602.05590/) (2026)
- [Int3DNet: Scene-Motion Cross Attention Network for 3D Intention Prediction in Mixed Reality](https://mlsanity.com/paper/2603.13355/) (2026)
- [Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot](https://mlsanity.com/paper/2604.21351/) (2026)

## Abstract (arXiv)

Replicating a user's pose from only wearable sensors is important for many AR/VR applications. Most existing methods for motion tracking avoid environment interaction apart from foot-floor contact due to their complex dynamics and hard constraints. However, in daily life people regularly interact with their environment, e.g. by sitting on a couch or leaning on a desk. Using Reinforcement Learning, we show that headset and controller pose, if combined with physics simulation and environment observations can generate realistic full-body poses even in highly constrained environments. The physics simulation automatically enforces the various constraints necessary for realistic poses, instead of manually specifying them as in many kinematic approaches. These hard constraints allow us to achieve high-quality interaction motions without typical artifacts such as penetration or contact sliding. We discuss three features, the environment representation, the contact reward and scene randomization, crucial to the performance of the method. We demonstrate the generality of the approach through various examples, such as sitting on chairs, a couch and boxes, stepping over boxes, rocking a chair and turning an office chair. We believe these are some of the highest-quality results achieved for motion tracking from sparse sensor with scene interaction.