Abstract not found

We describe an automatic technique for generating the motion of tendons and muscles under the skin of a traditionally animated character. This is achieved by integrating the traditional animation pipeline with a novel biomechanical simulator capable of dynamic simulation with complex routing constraints on muscles and tendons. We also describe an algorithm for computing the activation levels of muscles required to track the input animation. We demonstrate the results with several animations of the human hand.

Animated characters that move and gesticulate appropriately with spoken text are useful in a wide range of applications. Unfortunately, this class of movement is very difficult to generate, even more so when a unique, individual movement style is required. We present a system that, with a focus on arm gestures, is capable of producing full-body gesture animation for given input text in the style of a particular performer. Our process starts with video of a person whose gesturing style we wish to animate. A tool-assisted annotation process is performed on the video, from which a statistical model of the person’s particular gesturing style is built. Using this model and input text tagged with theme, rheme and focus, our generation algorithm creates a gesture script. As opposed to isolated singleton gestures, our gesture script specifies a stream of continuous gestures coordinated with speech. This script is passed to an animation system, which enhances the gesture description with additional detail. It then generates either kinematic or physically simulated motion based on this description. The system is capable of generating gesture animations for novel text that are consistent with a given performer’s style, as was successfully validated in an empirical user study.

Modifying motion capture to satisfy the constraints of new animation is difficult when contact is involved, and a critical problem for animation of hands. The compliance with which a character makes contact also reveals important aspects of the movement’s purpose. We present a new technique called interaction capture, for capturing these contact phenomena. We capture contact forces at the same time as motion, at a high rate, and use both to estimate a nominal reference trajectory and joint compliance. Unlike traditional methods, our method estimates joint compliance without the need for motorized perturbation devices. New interactions can then be synthesized by physically based simulation. We describe a novel position-based linear complementarity problem formulation that includes friction, breaking contact, and the compliant coupling between contacts at different fingers. The technique is validated using data from previous work and our own perturbation-based estimates.

Abstract — Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments. Index Terms — Grasp synthesis, hands, shape matching, grasp quality.

This paper introduces an optimization-based approach to synthesizing hand manipulations from a starting grasping pose. We describe an automatic method that takes as input an initial grasping pose and partial object trajectory, and produces as output physically plausible hand animation that effects the desired manipulation. In response to different dynamic situations during manipulation, our algorithm can generate a range of possible hand manipulations including changes in joint configurations, changes in contact points, and changes in the grasping force. Formulating hand manipulation as an optimization problem is key to our algorithm’s ability to generate a large repertoire of hand motions from limited user input. We introduce an objective function that accentuates the detailed hand motion and contacts adjustment. Furthermore, we describe an optimization method that solves for hand motion and contacts efficiently while taking into account long-term planning of contact forces. Our algorithm does not require any tuning of parameters, nor does it require any prescribed hand motion sequences.

Application to Grasping Robot manipulators typically rely on complete knowledge of object geometry in order to plan motions and compute grasps. However, when an object is not fully in view it can be difficult to form an ac-curate estimate of the object’s shape and pose, particularly when the object deforms. In this paper we describe a generative model of object geometry based on Mardia and Dryden’s “Probabilistic Procrustean Shape”, which captures both non-rigid deformations and object vari-ability in a class. We extend their shape model to the setting where point correspondences are unknown using Scott and Nowak’s CO-PAP framework. We use this model to recognize objects in a cluttered image and to infer their complete two-dimensional boundaries with a novel algorithm called OSIRIS. We show examples of learned models from image data and demonstrate how the models can be used by a manipulation planner to grasp objects in cluttered visual scenes. KEY WORDS—procrustean shape, robotic grasping, shape completion, occlusions, correspondences, MPEG-7 1.

Dynamic simulation is a promising complement to kinematic motion synthesis, particularly in cases where simulated characters need to respond to unpredictable interactions. Moving beyond simple rag-doll effects, though, requires dynamic control. The main issue with dynamic control is that there are no standardized techniques that allow an animator to precisely specify the timing of the motion while still providing natural response to external disturbances. The few proposed techniques that address this problem are based on heuristically or manually tuning proportional-derivative (PD) control parameters and do not generalize easily. We propose an approach to dynamic character control that is able to honor timing constraints, to provide naturallooking motion and to allow for realistic response to perturbations. Our approach uses traditional PD control to interpolate between key-frames. The key innovation is that the parameters of the PD controllers are computed for each joint analytically. By continuously updating these parameters over time, the controller is able to respond naturally to both external perturbations and changes in the state of the character.

Figure 1: Our algorithm synthesizes detailed hand movements for a wide variety of objects. (Cyan and yellow dots indicate contacts between the object and the hand and between the object and the environemnt respectively. Red arrows indicate contact forces.) Capturing human activities that involve both gross full-body mo-tion and detailed hand manipulation of objects is challenging for standard motion capture systems. We introduce a new method for creating natural scenes with such human activities. The input to our method includes motions of the full-body and the objects acquired simultaneously by a standard motion capture system. Our method then automatically synthesizes detailed and physically plausible hand manipulation that can seamlessly integrate with the input mo-tions. Instead of producing one “optimal ” solution, our method presents a set of motions that exploit a wide variety of manipula-tion strategies. We propose a randomized sampling algorithm to search for as many as possible visually diverse solutions within the computational time budget. Our results highlight complex strate-gies human hands employ effortlessly and unconsciously, such as static, sliding, rolling, as well as finger gaits with discrete reloca-tion of contact points.

Abstract not found