Flow visualizations generated by our H-MoRe and seven SoTA optical flow estimation algorithms.
Primary differences are marked and zoomed with red boxes and arrows.
We introduce H-MoRe, an innovative pipeline designed to learn precise, human-centered motion representations.
Our approach dynamically retains essential human motion features while filtering out background noise. Unlike traditional methods that rely on fully supervised learning with synthetic data, H-MoRe employs a self-supervised learning paradigm directly from real-world scenarios, incorporating both human pose and body shape information.
Drawing inspiration from kinematics, H-MoRe encodes absolute and relative movements of body points into a matrix representation, termed world-local flows, to capture subtle motion details. This method provides a detailed understanding of human motion, making it highly adaptable to various action-based applications.
Models and code will be made publicly available upon publication.
Flow visualizations generated by our H-MoRe and seven SoTA optical flow estimation algorithms.
Primary differences are marked and zoomed with red boxes and arrows.
Use this slider here to view the H-MoRe inference results for each frame.
Using H-MoRe as motion representation, we can boost the performance of gait recognition.
Higher values indicate better performance across all metrics.