Automated body modeling from video sequences

Synthetic modeling of human bodies and the simulation of motion is a long-standing problem in animation and much work is involved before a near-realistic performance can be achieved. At present, it takes an experienced designer a very long time to build a complete and realistic model that closely resembles a specific person. Our ultimate goal is to automate the process and to produce realistic animation models given a set of video sequences. In this paper we show that, given video sequences of a person moving in front of the camera, we can recover shape information and joint locations. Both of which are essential to instantiate a complete and realistic model that closely resembles a specific person and without knowledge about the position of the articulations a character cannot be animated. This is achieved with minimal human intervention. The recovered shape and motion parameters can be used to reconstruct the original movement or to allow other animation models to mimic the subject's action

Skeleton-based motion capture for robust reconstruction of human motion

Optical motion capture provides an impressive ability to replicate gestures. However, even with a highly professional system there are many instances where crucial markers are occluded or when the algorithm confuses the trajectory of one marker with that of another. This requires much editing work on the part of the animator before the virtual characters are ready for their screen debuts. In this paper, we present an approach to increasing the robustness of a motion capture system by using a sophisticated anatomic human model. It includes a precise description of the skeleton's mobility and an approximated envelope. It allows us to accurately predict the 3-D location and visibility of markers, thus significantly increasing the robustness of marker tracking and assignment, and drastically reducing-or even eliminating-the need for human intervention during the 3D reconstruction proces

Using skeleton-based tracking to increase the reliability of optical motion capture

Optical motion capture provides an impressive ability to replicate gestures. However, even with a highly professional system there are many instances where crucial markers are occluded or when the algorithm confuses the trajectory of one marker with that of another. This requires much editing work on the user's part before the complete animation is ready for use. Here, the authors present an approach to increasing the robustness of a motion capture system by using an anatomical human model. It includes a reasonably precise description of the skeleton's mobility and an approximated envelope. It allows the authors to accurately predict the 3-D location and visibility of markers, thus significantly increasing the robustness of the marker tracking and assignment, and drastically reducing-or even eliminating-the need for human intervention during the 3-D reconstruction proces

Local and global skeleton fitting techniques for optical motion capture

Identifying a precise anatomic skeleton is important in order to ensure high-quality motion capture. In this paper, we discuss two skeleton-fitting techniques based on 3D optical marker data. First, a local technique is proposed, based on relative marker trajectories. Then it is compared to a global optimization of a skeleton model. Various proposals are made to handle the skin deformation proble

Modeling human bodies from video sequences

In this paper, we show that, given video sequences of a moving person acquired with a multi-camera system, we can track joint locations during the movement and recover shape information. We outline techniques for fitting a simplified model to the noisy 3-D data extracted from the images and a new tracking process based on least squares matching is presented. The recovered shape and motion parameters can be used to either reconstruct the original sequence or to allow other animation models to mimic the subject's actions. Our ultimate goal is to automate the process of building complete and realistic animation models of humans, given a set of video sequence