Jovan Popovic

Articulated hand-tracking systems have been widely used in virtual reality but are rarely deployed in consumer applications due to their price and complexity. In this paper, we propose an easy-to-use and inexpensive system that facilitates 3-D articulated user-input using the hands. Our approach uses a single camera to track a hand wearing an ordinary cloth glove that is imprinted with a custom pattern. The pattern is designed to simplify the pose estimation problem, allowing us to employ a nearest-neighbor approach to track hands at interactive rates. We describe several proof-of-concept applications enabled by our system that we hope will provide a foundation for new interactions in modeling, animation control and augmented reality.

Transferring existing mesh deformation from one character to another is a simple way to accelerate the laborious process of mesh animation. In many cases, it is useful to preserve the semantic characteristics of the motion instead of its literal deformation. For example, when applying the walking motion of a human to a flamingo, the knees should bend in the opposite direction. Semantic deformation transfer accomplishes this task with a shape space that enables interpolation and projection with standard linear algebra. Given several example mesh pairs, semantic deformation transfer infers a correspondence between the shape spaces of the two characters. This enables automatic transfer of new poses and animations.

Keyframe animation is a common technique to generate animations of deformable characters and other soft bodies. With spline interpolation, however, it can be difficult to achieve secondary motion effects such as plausible dynamics when there are thousands of degrees of freedom to animate. Physical methods can provide more realism with less user effort, but it is challenging to apply them to quickly create specific animations that closely follow prescribed animator goals. We present a fast space-time optimization method to author physically based deformable object simulations that conform to animator-specified keyframes.

Details in mesh animations are difficult to generate but they have great impact on visual quality. In this work, we demonstrate a practical software system for capturing such details from multi-view video recordings. Given a stream of synchronized video images that record a human performance from multiple viewpoints and an articulated template of the performer, our system captures the motion of both the skeleton and the shape. The output mesh animation is enhanced with the details observed in the image silhouettes. For example, a performance in casual loose-fitting clothes will generate mesh animations with flowing garment motions. We accomplish this with a fast pose tracking method followed by nonrigid deformation of the template to fit the silhouettes.

Commercial motion-capture systems produce excellent in-studio reconstructions, but offer no comparable solution for acquisition in everyday environments. We present a system for acquiring motions almost anywhere. This wearable system gathers ultrasonic time-of-flight and inertial measurements with a set of inexpensive miniature sensors worn on the garment. After recording, the information is combined using an Extended Kalman Filter to reconstruct joint configurations of a body. Experimental results show that even motions that are traditionally difficult to acquire are recorded with ease within their natural settings.

Animating an articulated 3D character currently requires manual rigging to specify its internal skeletal structure and to define how the input motion deforms its surface. We present a method for animating characters automatically. Given a static character mesh and a generic skeleton, our method adapts the skeleton to the character and attaches it to the surface, allowing skeletal motion data to animate the character. Because a single skeleton can be used with a wide range of characters, our method, in conjunction with a library of motions for a few skeletons, enables a user-friendly animation system for novices and children. Our prototype implementation, called Pinocchio, typically takes under a minute to rig a character on a modern midrange PC.

Enveloping, or the mapping of skeletal controls to the deformations of a surface, is key to driving realistic animated characters. Despite its widespread use, enveloping still relies on slow or inaccurate deformation methods. We propose a method that is both fast, accurate and example-based. Our technique introduces a rotational regression model that captures common skinning deformations such as muscle bulging, twisting, and challenging areas such as the shoulders. Our improved treatment of rotational quantities is made practical by model reduction that ensures real-time solution of leastsquares problems, independent of the mesh size.

Standing is a fundamental skill mastered by humans and animals alike. Although easy for adults, it requires careful and deliberate manipulation of contact forces. The variation in contact confguration (e.g., standing on one foot, on uneven ground, or while holding on for support) presents a diffcult challenge for interactive simulation of humans and animals, especially while performing tasks in the presence of external disturbances. We describe an analytic approach for control of standing in three-dimensional simulations based upon local optimization. At any point in time, the control system solves a quadratic program to compute actuation by maximizing the performance of multiple motion objectives subject to constraints imposed by actuation limits and contact configuration.

Time warping allows users to modify timing without affecting poses. It has many applications in animation systems for motion editing, such as refining motions to meet new timing constraints or modifying the acting of animated characters. However, time warping typically requires many manual adjustments to achieve the desired results. We present a technique which simplifies this process by allowing time warps to be guided by a provided reference motion. Given few timing constraints, it computes a warp that both satisfies these constraints and maximizes local timing similarities to the reference. The algorithm is fast enough to incorporate into standard animation workflows.