# ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training

arXiv: https://arxiv.org/abs/2508.01850 · HTML: https://mlsanity.com/paper/2508.01850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 72 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion](https://mlsanity.com/paper/2312.07531/) (2023)
- [MotionBERT: A Unified Perspective on Learning Human Motion Representations](https://mlsanity.com/paper/2210.06551/) (2022)
- [SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2309.17448/) (2023)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time](https://mlsanity.com/paper/2008.08880/) (2020)
- [FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis](https://mlsanity.com/paper/2405.15763/) (2024)
- [Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors](https://mlsanity.com/paper/2312.02196/) (2023)
- [MobilePoser: Real-Time Full-Body Pose Estimation and 3D Human Translation from IMUs in Mobile Consumer Devices](https://mlsanity.com/paper/2504.12492/) (2025)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [SATO: Stable Text-to-Motion Framework](https://mlsanity.com/paper/2405.01461/) (2024)
- [HyperVQ: MLR-based Vector Quantization in Hyperbolic Space](https://mlsanity.com/paper/2403.13015/) (2024)
- [GTN-Bailando: Genre Consistent Long-Term 3D Dance Generation based on Pre-trained Genre Token Network](https://mlsanity.com/paper/2304.12704/) (2023)
- [PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation](https://mlsanity.com/paper/2302.00391/) (2023)
- [Learning Anthropometry from Rendered Humans](https://mlsanity.com/paper/2101.02515/) (2021)
- [Selecting the motion ground truth for loose-fitting wearables: benchmarking optical MoCap methods](https://mlsanity.com/paper/2307.11881/) (2023)
- [SeamPose: Repurposing Seams as Capacitive Sensors in a Shirt for Upper-Body Pose Tracking](https://mlsanity.com/paper/2406.11645/) (2024)
- [IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture](https://mlsanity.com/paper/2402.08923/) (2024)
- [SitPose: Real-Time Detection of Sitting Posture and Sedentary Behavior Using Ensemble Learning With Depth Sensor](https://mlsanity.com/paper/2412.12216/) (2024)
- [TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition](https://mlsanity.com/paper/2505.02052/) (2025)

## Abstract (arXiv)

Prolonged seated activity is increasingly common in modern environments, raising concerns around musculoskeletal health, ergonomics, and the design of responsive interactive systems. Existing posture sensing methods such as vision-based or wearable approaches face limitations including occlusion, privacy concerns, user discomfort, and restricted deployment flexibility. We introduce ChairPose, the first full body, wearable free seated pose estimation system that relies solely on pressure sensing and operates independently of chair geometry. ChairPose employs a two stage generative model trained on pressure maps captured from a thin, chair agnostic sensing mattress. Unlike prior approaches, our method explicitly incorporates chair morphology into the inference process, enabling accurate, occlusion free, and privacy preserving pose estimation. To support generalization across diverse users and chairs, we introduce a physics driven data augmentation pipeline that simulates realistic variations in posture and seating conditions. Evaluated across eight users and four distinct chairs, ChairPose achieves a mean per joint position error of 89.4 mm when both the user and the chair are unseen, demonstrating robust generalization to novel real world generalizability. ChairPose expands the design space for posture aware interactive systems, with potential applications in ergonomics, healthcare, and adaptive user interfaces.