This paper presents GoPose, a 3D skeleton-based human pose estimation system that uses WiFi devices at home. Our system leverages the WiFi signals reflected off the human body for 3D pose estimation. In contrast to prior systems that need specialized hardware or dedicated sensors, our system does not require a user to wear or carry any sensors and can reuse the WiFi devices that already exist in a home environment for mass adoption. To realize such a system, we leverage the 2D AoA spectrum of the signals reflected from the human body and the deep learning techniques. In particular, the 2D AoA spectrum is proposed to locate different parts of the human body as well as to enable environment-independent pose estimation. Deep learning is incorporated to model the complex relationship between the 2D AoA spectrums and the 3D skeletons of the human body for pose tracking. Our evaluation results show GoPose achieves around 4.7cm of accuracy under various scenarios including tracking unseen activities and under NLoS scenarios.
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Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation
In this paper, we propose a pose grammar to tackle the problem
of 3D human pose estimation. Our model directly takes
2D pose as input and learns a generalized 2D-3D mapping
function. The proposed model consists of a base network
which efficiently captures pose-aligned features and a hierarchy
of Bi-directional RNNs (BRNN) on the top to explicitly
incorporate a set of knowledge regarding human body configuration
(i.e., kinematics, symmetry, motor coordination). The
proposed model thus enforces high-level constraints over human
poses. In learning, we develop a pose sample simulator
to augment training samples in virtual camera views, which
further improves our model generalizability. We validate our
method on public 3D human pose benchmarks and propose a
new evaluation protocol working on cross-view setting to verify
the generalization capability of different methods.We empirically
observe that most state-of-the-art methods encounter
difficulty under such setting while our method can well handle
such challenges.
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- Award ID(s):
- 1657600
- NSF-PAR ID:
- 10056961
- Date Published:
- Journal Name:
- AAAI Conference on Artificial Intelligence
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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