Motion Control



Understanding and synthesizing motions for interactive virtual humans is one of the main challenges in computer animation. On one hand, physics-based simulation of characters offers automation and allows characters to interact with unpredictable events in their environment. On the other hand, techniques based on reusing recorded motions (mocap) offer precise control, and high fidelity. We have contributed to both families of techniques, as well as proposed hybrid approaches.

Selected publications
and demos:


  1. 1.Complex Networks of Simple Neurons for Bipedal Locomotion”, Brian Allen, Petros Faloutsos, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4457-4462, 2009.

    Abstract
    :

    Fluid bipedal locomotion remains a significant challenge for humanoid robotics. Recently bio-inspired approaches have made significant progress using small numbers of tightly coupled neurons, called central pattern generators (CPGs). Our approach exchanges complexity of the neuron model for complexity in the network, gradually building a network of simple neurons capable of complex behaviors. We show this approach generates controllers de novo that are able to control 3D bipedal locomotion up to 10 meters. This results holds for robots with human-proportionate morphologies across 95% of normal human variation. The resulting networks are then examined to discover neural structures that arise unusually often, lending some insight into the workings of otherwise opaque controllers.

    Video of the presentation slides at IROS:





  2. 2.On the Beat! Timing and Tension for Dynamic Characters”, Brian Allen, Derek Chu, Ari Shapiro, Petros Faloutsos, ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 2007, pp. 239-247.

    Abstract
    :

    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 natural- looking 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.

    Video demo:





  3. 3.Flipping with Physics: Motion Editing for Acrobatics”, Anna Majkowska, Petros Faloutsos, ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 2007, pp. 35-44.

    Abstract
    :

    Complex acrobatic stunts, such as double or triple flips, can be performed only by highly skilled athletes. On the other hand, simpler tricks, such as single-flip jumps, are relatively easy to master. We present a method for creating complex, multi-flip ballistic motions from simple, single-flip jumps. Our approach also allows an animator to interact with the system by introducing modifications to a ballistic phase of a motion. Our method automatically adjusts motion trajectories, to assure physical validity of the motion after the modifications. The presented technique is efficient and produces physically valid results without resorting to computationally expensive optimization. To validate our approach we present the results of a study of user sensitivity to errors in angular momentum and take-off angle. The study shows that small changes of these parameters introduced by our method are not perceptible to a viewer.

    Video Demo:





  4. 4.Interactive Motion Correction and Object Manipulation”, Ari Shapiro, Marcelo Kallmann, Petros Faloutsos, ACM SIGGRAPH, Symposium on Interactive 3D Graphics and Games, April 2007,pp.137-144.

    Abstract
    :

    Editing recorded motions to make them suitable for different sets of environmental constraints is a general and difficult open problem. In this paper we solve a significant part of this problem by modifying full-body motions with an interactive randomized motion planner. Our method is able to synthesize collision-free motions for specified linkages of multiple animated characters in synchrony with the characters’ full-body motions. The proposed method runs at interactive speed for dynamic environments of realistic complexity. We demonstrate the effectiveness of our interactive motion editing approach with two important applications: (a) motion correction (to remove collisions) and (b) synthesis of realistic object manipulation sequences on top of locomotion.

    Video Demo:





  5. 5.Composable Controllers for Physics-Based Character Animation”, Petros Faloutsos, Michiel van de Panne and Demetri Terzopoulos, Los Angeles, August, SIGGRAPH 2001, pp. 251-260


    Abstract
    :

    An ambitious goal in the area of physics-based computer animation is the creation of virtual actors that autonomously synthesize realistic human motions and possess a broad repertoire of lifelike motor skills. To this end, the control of dynamic, anthropomorphic figures subject to gravity and contact forces remains a difficult open problem. We propose a framework for composing con- trollers in order to enhance the motor abilities of such figures. A key contribution of our composition framework is an explicit model of the “pre-conditions” under which motor controllers are expected to function properly. We demonstrate controller composition with pre-conditions determined not only manually, but also automatically based on Support Vector Machine (SVM) learning theory. We evaluate our composition framework using a family of controllers capable of synthesizing basic actions such as balance, protective stepping when balance is disturbed, protective arm reactions when falling, and multiple ways of standing up after a fall. We further- more demonstrate these basic controllers working in conjunction with more dynamic motor skills within a prototype virtual stunt- person. Our composition framework promises to enable the community of physics-based animation practitioners to easily exchange motor controllers and integrate them into dynamic characters.

    Video Demo (has audio):