Ralf Plänkers

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 skeletons 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 process.

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 problem. Index Terms--skeleton fitting, motion capture, optical markers.

Tracking and Modeling People in Video Sequences

Tracking and modeling people from video sequences has become an increasingly important research topic, with applications including animation, surveillance, and sports medicine. In this paper, we propose a model-based 3-D approach to recovering both body shape and motion. It takes advantage of a sophisticated animation model to achieve both robustness and realism. Stereo sequences of people in motion serve as input to our system. From these, we extract a 212-D description of the scene and, optionally, silhouette edges. We propose an integrated framework to fit the model and to track the persons motion. The environment does not have to be engineered. We recover not only the motion but also a full animation model closely resembling the subject. We present results of our system on real sequences and we show the generic model adjusting to the person and following various kinds of motion.

Articulated soft objects for video-based body modeling

We develop a framework for 3-D shape and motion recovery of articulated deformable objects. We propose a formalism that incorporates the use of implicit surfaces into earlier robotics approaches that were designed to handle articulated structures. We demonstrate its effectiveness for human body modeling from video sequences. Our method is both robust and generic. It could easily be applied to other shape and motion recovery problems.

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 users part before the complete animation is ready for use. In this paper, we 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 skeletons 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 the marker tracking and assignment, and drastically reducing--or even eliminating--the need for human intervention during the 3-D reconstruction process.

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 subjects actions.