Silhouette-Based 3D Human Pose Estimation Using a Single Wrist-Mounted 360° Camera

Hori, Ryosuke; Hachiuma, Ryo; Isogawa, Mariko; Mikami, Dan; Saitō, Hideo

doi:10.1109/access.2022.3177623

Cited by 3 publications

References 51 publications

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EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors

Zhou

Habermann

et al. 2023

ACM Trans. Graph.

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Human and environment sensing are two important topics in Computer Vision and Graphics. Human motion is often captured by inertial sensors, while the environment is mostly reconstructed using cameras. We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera. On one hand, inertial mocap suffers from large translation drift due to the lack of the global positioning signal. EgoLo-cate leverages image-based simultaneous localization and mapping (SLAM) techniquesto locate the human in the reconstructed scene. Onthe other hand, SLAM often fails when the visual feature is poor. EgoLocate involves inertial mocap to provide a strong prior for the camera motion. Experiments show that localization, a key challenge for both two fields, is largely improved by our technique, compared with the state of the art of the two fields. Our codes are available for research at https://xinyu-yi.github.io/EgoLocate/.

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EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors

Zhou

Habermann

et al. 2023

ACM Trans. Graph.

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BodyTouch

Cheng,

Chan

2023

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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This paper presents a study on the touch precision of an eye-free, body-based interface using on-body and near-body touch methods with and without skin contact. We evaluate user touch accuracy on four different button layouts. These layouts progressively increase the number of buttons between adjacent body joints, resulting in 12, 20, 28, and 36 touch buttons distributed across the body. Our study indicates that the on-body method achieved an accuracy beyond 95% for the 12- and 20-button layouts, whereas the near-body method only for the 12-button layout. Investigating user touch patterns, we applied SVM classifiers, which boost both the on-body and near-body methods to support up to the 28-button layouts by learning individual touch patterns. However, using generalized touch patterns did not significantly improve accuracy for more complex layouts, highlighting considerable differences in individual touch habits. When evaluating user experience metrics such as workload perception, confidence, convenience, and willingness-to-use, users consistently favored the 20-button layout regardless of the touch technique used. Remarkably, the 20-button layout, when applied to on-body touch methods, does not necessitate personal touch patterns, showcasing an optimal balance of practicality, effectiveness, and user experience without the need for trained models. In contrast, the near-body touch targeting the 20-button layout needs a personalized model; otherwise, the 12-button layout offers the best immediate practicality.

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Privacy-Preserving Human Behavior Estimation with Sparse Sensor Measurements

Isogawa

2023

JSAP-Optica Joint Symposia 2023 Abstracts

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Most conventional methods for person behavior estimation acquire information that can easily identify the person, such as images that include information on the face and clothing or acoustic information that includes voice, speech content, musical instrument playing sound, etc. Instead, we have been tack-ling proposed methods that do not acquire them towards privacy-preserving human behavior estimation. Specifically, we have been working on 3D human pose estimation with binary images [1], laser reflection [2], acoustic signals [3], and human mesh estimation with event data [4], making it possible to estimate human behavior in various situations while protecting personal information. In this talk, I will introduce our recent work on these topics.

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Silhouette-Based 3D Human Pose Estimation Using a Single Wrist-Mounted 360° Camera

Cited by 3 publications

References 51 publications

EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors

EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors

BodyTouch

Privacy-Preserving Human Behavior Estimation with Sparse Sensor Measurements

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