Motion constraint

In this paper, we propose a hybrid postural control approach taking advantage of data-driven and goal-oriented methods while overcoming their limitations. In particular, we take advantage of the latent space characterizing a given motion database. We introduce a motion constraint operating in the latent space to benefit from its much smaller dimension compared to the joint space. This allows its transparent integration into a Prioritized Inverse Kinematics framework. If its priority is high the constraint may restrict the solution to lie within the motion database space. We are more interested in the alternate case of an intermediate priority level that channels the postural control through a spatiotemporal pattern representative of the motion database while achieving a broader range of goals. We illustrate this concept with a sparse database of large range full-body reach motion

Real-Time Joint Coupling of the Spine for Inverse Kinematics

In this paper we propose a simple model for the coupling behavior of the human spine for an inverse kinematics framework. Our spine model exhibits anatomically correct motions of the vertebrae of virtual mannequins by coupling standard swing and revolute joint models. The adjustement of the joints is made with several simple (in)equality constraints, resulting in a reduction of the solution space dimensionality for the inverse kinematics solver. By reducing the solution space dimensionality to feasible spine shapes, we prevent the inverse kinematics algorithm from providing infeasible postures for the spine.In this paper, we exploit how to apply these simple constraints to the human spine by a strict decoupling of the swing and torsion motion of the vertebrae. We demonstrate the validity of our approach on various experiments

Real-Time Joint Coupling of the Spine for Inverse Kinematics

In this paper we propose a simple model for the coupling behavior of the human spine for an inverse kinematics framework. Our spine model exhibits anatomically correct motions of the vertebrae of virtual mannequins by coupling standard swing and revolute joint models. The adjustement of the joints is made with several simple (in)equality constraints, resulting in a reduction of the solution space dimensionality for the inverse kinematics solver. By reducing the solution space dimensionality to feasible spine shapes, we prevent the inverse kinematics algorithm from providing infeasible postures for the spine.In this paper, we exploit how to apply these simple constraints to the human spine by a strict decoupling of the swing and torsion motion of the vertebrae. We demonstrate the validity of our approach on various experiments

Immersive singularity-free full-body interactions with reduced marker set

Despite the large success of games grounded on movement-based interactions the current state of full-body motion capture technologies still prevents the exploitation of precise interactions with complex environments. The first key requirement in the line of work we present here is to ensure a precise spatial correspondence between the user and the Avatar. For that purpose, we build upon our past effort in human postural control with a prioritized inverse kinematics (PIK) framework. One of its key advantages is to ease the dynamic-and priority-based combination of multiple conflicting constraints such as ensuring the balance and reaching a goal. However, its reliance on a linearized approximation of the problem makes it vulnerable to the well-known full extension singularity of the limbs. We address this issue by presenting a new type of 1D analytic constraint that smoothly integrates within the PIK framework under the name of FLEXT constraint (for FLexion-EXTension constraint). We further ease the full-body interaction by combining this new constraint with a recently introduced motion constraint to exploit the data-based synergy of full-body reach motions. The combination of both techniques allows immersive full-body interactions with a small set of active optical marker. Copyright (C) 2010 John Wiley & Sons, Ltd

Progressive Clamping

Abstract—In this paper we propose the progressive clamping method to better model the kinematic anisotropy of joint limits for virtual mannequins or robots. Like recent approaches our method damps only the joints ’ variation component heading towards the limits. In addition we propose to dynamically express the corrective joint variation as a highest priority constraint that naturally extends the management of inequality constraints. This process is iterative within linear computing cost of the number of independent joints. We present how our approach is exploited for the major classes of rotation joints from one and up to three degrees of freedom. A comparison with other joint limit avoidance methods is given. We demonstrate the validity of our approach on various experiments targeting on the control of virtual mannequins.

Environment-Aware Postural Control of Virtual Humans for Real-time Applications

Interactive control of a virtual character through full body movement has a wide range of applications. However, there is a need for systems that accurately reproduce the motion of a performer while accounting for surrounding obstacles. We propose an approach based on an efficient Prioritized Inverse Kinematics constraint solver. The inputs to the system are the acquired positions of relevant body parts. These positions are tracked by the virtual character thanks to a set of kinematic constraints. At the same time a special constraint, which we call “observer”, is attached to body parts that must be checked for collisions. An observer acts by smoothly damping the motion of its controlled body part towards nearby obstacles, thus preventing future collisions from taking place