Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors
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Abstract (arXiv)
We introduce (HPS) Human POSEitioning System, a method to recover the full 3D pose of a human registered with a 3D scan of the surrounding environment using wearable sensors. Using IMUs attached at the body limbs and a head mounted camera looking outwards, HPS fuses camera based self-localization with IMU-based human body tracking. The former provides drift-free but noisy position and orientation estimates while the latter is accurate in the short-term but subject to drift over longer periods of time. We show that our optimization-based integration exploits the benefits of the two, resulting in pose accuracy free of drift. Furthermore, we integrate 3D scene constraints into our optimization, such as foot contact with the ground, resulting in physically plausible motion. HPS complements more common third-person-based 3D pose estimation methods. It allows capturing larger recording volumes and longer periods of motion, and could be used for VR/AR applications where humans interact with the scene without requiring direct line of sight with an external camera, or to train agents that navigate and interact with the environment based on first-person visual input, like real humans. With HPS, we recorded a dataset of humans interacting with large 3D scenes (300-1000 sq.m) consisting of 7 subjects and more than 3 hours of diverse motion. The dataset, code and video will be available on the project page: http://virtualhumans.mpi-inf.mpg.de/hps/ .
Key facts. Cited by 137 papers in the map (data through 7 Oct 2026). Builds on 37 of its 80 references that are in the map. First posted on arXiv 31 Mar 2021.
Builds on
37 of 80 references.
References · 37
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
- 2019Habitat: A Platform for Embodied AI Researchcited by 1,163
- 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
- 2017End-to-end Recovery of Human Shape and Posecited by 883
- 2018From Coarse to Fine: Robust Hierarchical Localization at Large Scalecited by 556
- 2018Gibson Env: Real-World Perception for Embodied Agentscited by 477
- 2019Resolving 3D Human Pose Ambiguities with 3D Scene Constraintscited by 294
- 2019Multi-Garment Net: Learning to Dress 3D People from Imagescited by 244
- 2020Long-term Human Motion Prediction with Scene Contextcited by 191
- 2018Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimationcited by 185
- 2019Understanding the Limitations of CNN-based Absolute Camera Pose Regressioncited by 177
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- 2020Visual Camera Re-Localization from RGB and RGB-D Images Using DSACcited by 176
- 2019Tex2Shape: Detailed Full Human Body Geometry From a Single Imagecited by 166
- 2018CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAMcited by 151
- 2020TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Stylecited by 148
- 2016Image-based localization using LSTMs for structured feature correlationcited by 136
- 2020Combining Implicit Function Learning and Parametric Models for 3D Human Reconstructioncited by 133
- 2019Generating 3D People in Scenes without Peoplecited by 127
- 2016PlaNet - Photo Geolocation with Convolutional Neural Networkscited by 114
- 2017Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUscited by 108
- 20203D Human Motion Estimation via Motion Compression and Refinementcited by 98
- 2019Ego-Pose Estimation and Forecasting as Real-Time PD Controlcited by 87
- 2019Putting Humans in a Scene: Learning Affordance in 3D Indoor Environmentscited by 72
- 2019Expert Sample Consensus Applied to Camera Re-Localizationcited by 70
- 2018Mo2Cap2: Real-time Mobile 3D Motion Capture with a Cap-mounted Fisheye Cameracited by 67
- 2020Learning to Transfer Texture from Clothing Images to 3D Humanscited by 58
- 2020SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Cameracited by 49
- 2016Going Deeper into First-Person Activity Recognitioncited by 48
- 2019Estimating 3D Motion and Forces of Person-Object Interactions from Monocular Videocited by 48
- 2016Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Videocited by 45
- 2018Binge Watching: Scaling Affordance Learning from Sitcomscited by 41
- 2019SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representationscited by 37
- 2018Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascadecited by 35
- 2020MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimationcited by 35
- 2018Leveraging Deep Visual Descriptors for Hierarchical Efficient Localizationcited by 29
- 2016Pose for Action - Action for Posecited by 16
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Citing papers per year: 2021: 7; 2022: 19; 2023: 29; 2024: 40; 2025: 26; 2026: 16.
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