A tutorial on motion capture driven character animation

Motion capture (MoCap) is an increasingly important technique to create realistic human motion for animation. However MoCap data are noisy, the resulting animation is often inaccurate and unrealistic without elaborate manual processing of the data. In this paper, we will discuss practical issues for MoCap driven character animation, particularly when using commercial toolkits. We highlight open topics in this field for future research. MoCap animations created in this project will be demonstrated at the conference

Dynamics and Control of Humanoid Robots: A Geometrical Approach

his paper reviews modern geometrical dynamics and control of humanoid robots. This general Lagrangian and Hamiltonian formalism starts with a proper definition of humanoid's configuration manifold, which is a set of all robot's active joint angles. Based on the `covariant force law', the general humanoid's dynamics and control are developed. Autonomous Lagrangian dynamics is formulated on the associated `humanoid velocity phase space', while autonomous Hamiltonian dynamics is formulated on the associated `humanoid momentum phase space'. Neural-like hierarchical humanoid control naturally follows this geometrical prescription. This purely rotational and autonomous dynamics and control is then generalized into the framework of modern non-autonomous biomechanics, defining the Hamiltonian fitness function. The paper concludes with several simulation examples. Keywords: Humanoid robots, Lagrangian and Hamiltonian formalisms, neural-like humanoid control, time-dependent biodynamicsComment: 27 pages, 9 figures, Late

Developing a 3D multi-body simulation tool to study dynamic behaviour of human scoliosis

Ph.DDOCTOR OF PHILOSOPH

A Data-Driven Appearance Model for Human Fatigue

Humans become visibly tired during physical activity. After a set of squats, jumping jacks or walking up a flight of stairs, individuals start to pant, sweat, loose their balance, and flush. Simulating these physiological changes due to exertion and exhaustion on an animated character greatly enhances a motion’s realism. These fatigue factors depend on the mechanical, physical, and biochemical function states of the human body. The difficulty of simulating fatigue for character animation is due in part to the complex anatomy of the human body. We present a multi-modal capturing technique for acquiring synchronized biosignal data and motion capture data to enhance character animation. The fatigue model utilizes an anatomically derived model of the human body that includes a torso, organs, face, and rigged body. This model is then driven by biosignal output. Our animations show the wide range of exhaustion behaviors synthesized from real biological data output. We demonstrate the fatigue model by augmenting standard motion capture with exhaustion effects to produce more realistic appearance changes during three exercise examples. We compare the fatigue model with both simple procedural methods and a dense marker set data capture of exercise motions

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.Comment: 12 pages, Accepted at Eurographics 201