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41
Opensim : Open-source software to create and analyze dynamic simulations of movement
- Biomedical Engineering, IEEE Transactions on
, 1940
"... Abstract—Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simu-lations can also be used to identify the sources of pathological movement and establish a scientific basis for treat ..."
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Cited by 103 (5 self)
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Abstract—Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simu-lations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechan-ical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies. Index Terms—Computed muscle control, forward dynamic sim-ulation, musculoskeletal modeling, open-source software. I.
Musculotendon Simulation for Hand Animation
- TO APPEAR IN THE ACM SIGGRAPH CONFERENCE PROCEEDINGS
"... 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 const ..."
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Cited by 51 (5 self)
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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.
Data driven grasp synthesis using shape matching and task-based pruning
- IEEE Transactions on Visualization and Computer Graphics
, 2007
"... 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. ..."
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Cited by 35 (1 self)
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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.
Capture and modeling of non-linear heterogeneous soft tissue
- in Proc. ACM SIGGRAPH, 2009
"... Figure 1: From left to right: Force-and-deformation capture of a non-linear heterogeneous pillow; synthesized deformation with fitted material parameters; and interactive deformation synthesized with our soft tissue modeling technique. This paper introduces a data-driven representation and modeling ..."
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Cited by 28 (7 self)
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Figure 1: From left to right: Force-and-deformation capture of a non-linear heterogeneous pillow; synthesized deformation with fitted material parameters; and interactive deformation synthesized with our soft tissue modeling technique. This paper introduces a data-driven representation and modeling technique for simulating non-linear heterogeneous soft tissue. It simplifies the construction of convincing deformable models by avoiding complex selection and tuning of physical material param-eters, yet retaining the richness of non-linear heterogeneous behav-ior. We acquire a set of example deformations of a real object, and represent each of them as a spatially varying stress-strain re-lationship in a finite-element model. We then model the material by non-linear interpolation of these stress-strain relationships in strain-space. Our method relies on a simple-to-build capture sys-tem and an efficient run-time simulation algorithm based on incre-mental loading, making it suitable for interactive computer graphics applications. We present the results of our approach for several non-linear materials and biological soft tissue, with accurate agreement of our model to the measured data.
Dextrous manipulation from a grasping pose
- ACM Transactions on Graphics
"... 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 t ..."
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Cited by 27 (2 self)
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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.
Synthesis of detailed hand manipulations using contact sampling
- ACM Transactions on Graphics (Proc of SIGGRAPH
"... 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 bot ..."
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Cited by 18 (1 self)
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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.
HandNavigator: Hands-on Interaction for Desktop Virtual Reality
"... Figure 1: Left, a conceptual sketch of the HandNavigator device; center and right, examples of hands-on interaction with rigid and deformable virtual environment. This paper presents a novel interaction system, aimed at hands-on manipulation of digital models through natural hand gestures. Our syste ..."
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Cited by 14 (2 self)
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Figure 1: Left, a conceptual sketch of the HandNavigator device; center and right, examples of hands-on interaction with rigid and deformable virtual environment. This paper presents a novel interaction system, aimed at hands-on manipulation of digital models through natural hand gestures. Our system is composed of a new physical interaction device coupled with a simulated compliant virtual hand model. The physical interface consists of a SpaceNavigator, augmented with pressure sensors to detect directional forces applied by the user’s fingertips. This information controls the position, orientation, and posture of the virtual hand in the same way that the SpaceNavigator uses measured forces to animate a virtual frame. In this manner, user control does not involve fatigue due to reaching gestures or holding a desired hand shape. During contact, the user has a realistic visual feedback in the form of plausible interactions between the virtual hand and its environment. Our device is well suited to any situation where hand gesture, contact, or manipulation tasks need to be performed in virtual. We demonstrate the device in several simple virtual worlds and evaluate it through a series of user studies.
An Object-Dependent Hand Pose Prior from Sparse Training Data
"... In this paper, we propose a prior for hand pose estimation that integrates the direct relation between a manipulating hand and a 3d object. This is of particular interest for a variety of applications since many tasks performed by humans require hand-object interaction. Inspired by the ability of hu ..."
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Cited by 10 (7 self)
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In this paper, we propose a prior for hand pose estimation that integrates the direct relation between a manipulating hand and a 3d object. This is of particular interest for a variety of applications since many tasks performed by humans require hand-object interaction. Inspired by the ability of humans to learn the handling of an object from a single example, our focus lies on very sparse training data. We express estimated hand poses in local object coordinates and extract for each individual hand segment, the relative position and orientation as well as contact points on the object. The prior is then modeled as a spatial distribution conditioned to the object. Given a new object of the same object class and new hand dimensions, we can transfer the prior by a procedure involving a geometric warp. In our experiments, we demonstrate that the prior may be used to improve the robustness of a 3d hand tracker and to synthesize a new hand grasping a new object. For this, we integrate the prior into a unified belief propagation framework for tracking and synthesis. 1.
Controlling Physics-Based Characters Using Soft Contacts
"... In this paper, we investigate the impact of the deformable bodies on the control algorithms for physically simulated characters. We hypothesize that ignoring the effect of deformable bodies at the site of contact negatively affects the control algorithms, leading to less robust and unnatural charact ..."
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Cited by 9 (1 self)
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In this paper, we investigate the impact of the deformable bodies on the control algorithms for physically simulated characters. We hypothesize that ignoring the effect of deformable bodies at the site of contact negatively affects the control algorithms, leading to less robust and unnatural character motions. To verify the hypothesis, we introduce a compact representation for an articulated character with deformable soft tissue and develop a practical system to simulate two-way coupling between rigid and deformable bodies in a robust and efficient manner. We then apply a few simple and widely used control algorithms, such as pose-space tracking control, Cartesianspace tracking control, and a biped controller (SIMBICON), to simulate a variety of behaviors for both full-body locomotion and hand manipulation. We conduct a series of experiments to compare our results with the motion generated by these algorithms on a character comprising only rigid bodies. The evaluation shows that the character with soft contact can withstand larger perturbations in a more noisy environment, as well as produce more realistic motion.
