COMPUTER VISION
PhaseMP: Robust 3D Pose Estimation via Phase-conditioned Human Motion Prior
September 10, 2023
Abstract
We present a novel motion prior, called PhaseMP, modeling a probability distribution on pose transitions conditioned by a frequency domain feature extracted from a periodic autoencoder. The phase feature further enforces the pose transitions to be unidirectional (i.e. no backward movement in time), from which more stable and natural motions can be generated. Specifically, our motion prior can be useful for accurately estimating 3D human motions in the presence of challenging input data, including long periods of spatial and temporal occlusion, as well as noisy sensor measurements. Through a comprehensive evaluation, we demonstrate the efficacy of our novel motion prior, showcasing its superiority over existing state-of-the-art methods by a significant margin across various applications, including video-to-motion and motion estimation from sparse sensor data, and etc.
AUTHORS
Written by
Yuting Ye
Sebastian Starke
Jungdam Won
Mingyi Shi
Taku Komura
Publisher
ICCV
Research Topics
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