Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance

A novel approach of 3D human model segmentation is proposed, which is based on heat kernel signature and geodesic distance. Through calculating the heat kernel signature of the point clouds of human body model, the local maxima of thermal energy distribution of the model is found, and the set of feature points of the model is obtained. Heat kernel signature has affine invariability which can be used to extract the correct feature points of the human model in different postures. We adopt the method of geodesic distance to realize the hierarchical segmentation of human model after obtaining the semantic feature points of human model. The experimental results show that the method can overcome the defect of geodesic distance feature extraction. The human body models with different postures can be obtained with the model segmentation results of human semantic characteristics

A Riemannian Take on Human Motion Analysis and Retargeting

Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear interactions between their degrees of freedom. However, these dynamical effects remain mostly overlooked when studying the mechanisms of human movement generation. Inspired by recent works, we hypothesize that human motions are planned as sequences of geodesic synergies, and thus correspond to coordinated joint movements achieved with piecewise minimum energy. The underlying computational model is built on Riemannian geometry to account for the inertial characteristics of the body. Through the analysis of various human arm motions, we find that our model segments motions into geodesic synergies, and successfully predicts observed arm postures, hand trajectories, as well as their respective velocity profiles. Moreover, we show that our analysis can further be exploited to transfer arm motions to robots by reproducing individual human synergies as geodesic paths in the robot configuration space.Comment: Accepted for publication in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 202

On singular values decomposition and patterns for human motion analysis and simulation

We are interested in human motion characterization and automatic motion simulation. The apparent redun- dancy of the humanoid w.r.t its explicit tasks lead to the problem of choosing a plausible movement in the framework of redun- dant kinematics. This work explores the intrinsic relationships between singular value decomposition at kinematic level and optimization principles at task level and joint level. Two task- based schemes devoted to simulation of human motion are then proposed and analyzed. These results are illustrated by motion captures, analyses and task-based simulations. Pattern of singular values serve as a basis for a discussion concerning the similarity of simulated and real motions