Accurate Capture of 3D Full-body Motion Using A Single Camera

In the past decade, motion capture technologies have enabled tremendous advancement in creating realistic human characters for virtual worlds, performing biomechanical studies of human movement, and providing natural user interfaces for interacting with computers, robots, and machines. However, current motion capture technologies are often limited to high-end applications because they are restrictive, expensive, and require special skills to set up and operate. This dissertation explores a new generation of motion capture technologies that address such challenges. We focus our study on two important and challenging motion capture problems: high-fidelity motion capture using a single video camera and online motion capture using a single depth camera. We first introduce a new video-based motion capture technique for reconstructing physically realistic full-body motion from single-camera video streams such as Internet videos. During reconstruction, we leverage Newtonian physics, contact constraints, and 2D image measurements to simultaneously reconstruct full-body poses, joint torques, and contact forces. across an entire sequence. For online applications, we develop a motion capture system that accurately captures 3D full-body movements in real time using a single depth camera. Both systems are appealing for home use because they are low-cost, easy to set up, and allow for accurate motion capture even with significant occlusions. For both systems, we assess the quality of the reconstruction results by comparing against those created by a commercial optical motion capture system. We demonstrate the quality of the reconstructed motions created by our systems is comparable to commercial motion capture systems, but our systems are far less expensive, restrictive, and cumbersome. More information about this dissertation can be found in digital repository at Texas A&M University: http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10566

Human motion modeling and simulation by anatomical approach

To instantly generate desired infinite realistic human motion is still a great challenge in virtual human simulation. In this paper, the novel emotion effected motion classification and anatomical motion classification are presented, as well as motion capture and parameterization methods. The framework for a novel anatomical approach to model human motion in a HTR (Hierarchical Translations and Rotations) file format is also described. This novel anatomical approach in human motion modelling has the potential to generate desired infinite human motion from a compact motion database. An architecture for the real-time generation of new motions is also propose

Sketching-out virtual humans: From 2d storyboarding to immediate 3d character animation

Virtual beings are playing a remarkable role in today’s public entertainment, while ordinary users are still treated as audiences due to the lack of appropriate expertise, equipment, and computer skills. In this paper, we present a fast and intuitive storyboarding interface, which enables users to sketch-out 3D virtual humans, 2D/3D animations, and character intercommunication. We devised an intuitive “stick figurefleshing-outskin mapping” graphical animation pipeline, which realises the whole process of key framing, 3D pose reconstruction, virtual human modelling, motion path/timing control, and the final animation synthesis by almost pure 2D sketching. A “creative model-based method” is developed, which emulates a human perception process, to generate the 3D human bodies of variational sizes, shapes, and fat distributions. Meanwhile, our current system also supports the sketch-based crowd animation and the storyboarding of the 3D multiple character intercommunication. This system has been formally tested by various users on Tablet PC. After minimal training, even a beginner can create vivid virtual humans and animate them within minutes