Jehee Lee

Real-time control of three-dimensional avatars is an important problem in the context of computer games and virtual environments. Avatar animation and control is difficult, however, because a large repertoire of avatar behaviors must be made available, and the user must be able to select from this set of behaviors, possibly with a low-dimensional input device. One appealing approach to obtaining a rich set of avatar behaviors is to collect an extended, unlabeled sequence of motion data appropria

This paper presents a technique for adapting existing motion of a human-like character to have the desired features that are specified by a set of constraints. This problem can be typically formulated as a spacetime constraint problem. Our approach combines a hierarchical curve fitting technique with a new inverse kinematics solver. Using the kinematics solver, we can adjust the configuration of an articulated figure to meet the constraints in each frame. Through the fitting technique, the motio

Real-time control of three-dimensional avatars is an important problem in the context of computer games and virtual environments. Avatar animation and control is difficult, however, because a large repertoire of avatar behaviors must be made available, and the user must be able to select from this set of behaviors, possibly with a low-dimensional input device. One appealing approach to obtaining a rich set of avatar behaviors is to collect an extended, unlabeled sequence of motion data appropria

Creating controllable, responsive avatars is an important problem in computer games and virtual environments. Recently, large collections of motion capture data have been exploited for increased realism in avatar animation and control. Large motion sets have the advantage of accommodating a broad variety of natural human motion. However, when a motion set is large, the time required to identify an appropriate sequence of motions is the bottleneck for achieving interactive avatar control. In this

Capturing live motion has gained considerable attention in computer animation as an important motion generation technique. Canned motion data are comprised of both position and orientation components. Although a great number of signal processing methods are available for manipulating position data, the majority of these methods cannot be generalized easily to orientation data due to the inherent nonlinearity of the orientation space. In this paper, we present a new scheme that enables us to appl

In 3D space, orientations and rotations are not interchangeable. Therefore, we should represent them differently and appropriately.

Motion capturing is widely used for generating realistic motions. A 3D input device captures a sequence of 6 DOF rigid motion samples. The sampled data consists of two components, translation and orientation; the former is represented by a vector in R/sup 3/ and the latter by a rotation matrix in the rotation group, SO(3). Since the sequence of data contains sampling noises, the captured motion is now smooth and wiggles along the moving path. There are well-known fairing algorithms in Euclidean

Abstract Human facial gestures often exhibit such natural stochastic variations as how often the eyes blink, how often the eyebrows and the nose twitch, and how the head moves while speaking. The stochastic movements of facial features are key ingredients for generating convincing facial expressions. Although such small variations have been simulated using noise functions in many graphics applications, modulating noise functions to match natural variations induced from the affective states and t

Three-dimensional reconstruction of the femur is important for surgical planning in patients with cerebral palsy. This study aimed to reconstruct the three-dimensional femur shape from un-calibrated bi-planar radiographic images using self-calibration to allow for low-dose preoperative planning. The existing self-calibration techniques require anatomical landmarks that are clearly visible on bi-planar images, which are not available on the femur. In our newly developed method, the self-calibrati

ABSTRACT This paper presents a real‐time performance animation system that reproduces full‐body character animation based on sparse three‐dimensional (3D) motion sensors on a performer. Producing faithful character animation from this setting is a mathematically ill‐posed problem, because input data from the sensors are not sufficient to determine the full degrees of freedom of a character. Given the input data from 3D motion sensors, we select similar poses from a motion database and build an o

Abstract Markerless human pose recognition using a single‐depth camera plays an important role in interactive graphics applications and user interface design. Recent pose recognition algorithms have adopted machine learning techniques, utilizing a large collection of motion capture data. The effectiveness of the algorithms is greatly influenced by the diversity and variability of training data. We present a new sampling method that resamples a collection of human motion data to improve the pose

Data-driven animation using motion capture data has become a standard practice in character animation. A number of techniques have been developed to add flexibility on captured human motion data by editing joint trajectories, warping motion paths, blending a family of parameterized motions, splicing motion segments, and adapting motion to new characters and environments. Even with the abundance of motion capture data and the popularity of data-driven animation techniques, programming with motion