Capturing Detailed Deformations of Moving Human Bodies
He Chen, Hyojoon Park, Kutay Macit, Ladislav Kavan

TL;DR
This paper introduces a novel RGB-based motion capture system that automatically captures detailed 4D point trajectories of human bodies, including subtle deformations, without relying on traditional skeletal models or tracking.
Contribution
The method uniquely uses a passive suit with a checkerboard pattern and neural networks to localize and label points from 2D images, capturing detailed motion including muscle and flesh deformations.
Findings
Accurately captures complex human motions like yoga and gymnastics.
Automatically assigns unique labels to over 1000 points per frame.
Operates with standard RGB sensors and passive lighting.
Abstract
We present a new method to capture detailed human motion, sampling more than 1000 unique points on the body. Our method outputs highly accurate 4D (spatio-temporal) point coordinates and, crucially, automatically assigns a unique label to each of the points. The locations and unique labels of the points are inferred from individual 2D input images only, without relying on temporal tracking or any human body shape or skeletal kinematics models. Therefore, our captured point trajectories contain all of the details from the input images, including motion due to breathing, muscle contractions and flesh deformation, and are well suited to be used as training data to fit advanced models of the human body and its motion. The key idea behind our system is a new type of motion capture suit which contains a special pattern with checkerboard-like corners and two-letter codes. The images from our…
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Taxonomy
TopicsHuman Pose and Action Recognition · Human Motion and Animation · Advanced Vision and Imaging
