Biomechanically-inspired motion path editing

Lockwood, Noah; Singh, Karan

doi:10.1145/2019406.2019442

Cited by 16 publications

(12 citation statements)

References 27 publications

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“…Once the coordination function

scriptC

is ready, it can then be used to synthesize new variations of the same type of object interaction. We have designed our method to be applied on top of generic walking controllers [PhKS04, LS11], such that successful action coordination can be achieved with regular walking controllers. Our walking controller is based on a self‐concatenating deformable walking motion with feet constraints enforced by inverse kinematics (IK), which was developed as part of our previous work [JPFKS14, JPK16, JPK18].…”

Section: Real‐time Motion Synthesis With the CMmentioning

confidence: 99%

Detection and Synthesis of Full‐Body Environment Interactions for Virtual Humans

Juarez-Perez

Kallmann

2019

Computer Graphics Forum

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We present a new methodology for enabling virtual humans to autonomously detect and perform complex full‐body interactions with their environments. Given a parameterized walking controller and a set of motion‐captured example interactions, our method is able to detect when interactions can occur and to coordinate the detected upper‐body interaction with the walking controller in order to achieve full‐body mobile interactions in similar situations. Our approach is based on learning spatial coordination features from the example motions and on associating body‐environment proximity information to the body configurations of each performed action. Body configurations become the input to a regression system, which in turn is able to generate new interactions for different situations in similar environments. The regression model is capable of selecting, encoding and replicating key spatial strategies with respect to body coordination and management of environment constraints as well as determining the correct moment in time and space for starting an interaction. As a result, we obtain an interactive controller able to detect and synthesize coordinated full‐body motions for a variety of complex interactions requiring body mobility. Our results achieve complex interactions, such as opening doors and drawing in a wide whiteboard. The presented approach introduces the concept of learning interaction coordination models that can be applied on top of any given walking controller. The obtained method is simple and flexible, it handles the detection of possible interactions and is suitable for real‐time applications.

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“…Once the coordination function

scriptC

Section: Real‐time Motion Synthesis With the CMmentioning

confidence: 99%

Detection and Synthesis of Full‐Body Environment Interactions for Virtual Humans

Juarez-Perez

Kallmann

2019

Computer Graphics Forum

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“…Gleicher presented an intuitive method for editing motion data through the modification of motion trajectories . Lockwood and Singh proposed a method for motion editing based on a biomechanical heuristic that describes the relationship between stride length, path curvature, and moving speed . To produce a turn, these methods mainly modify a root (pelvis) trajectory spatially or temporally.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, at least one of the feet should be in contact with the ground to control the COM movements. Taking the root of the character as an approximation for the COM as in , our transformation scheme deforms the root trajectory of the initial motion only at contact phases, in which at least one of the feet is in contact with the ground. Accordingly, we modify functions, θ (.)…”

Section: Automatic Motion Transformationmentioning

confidence: 99%

“…As mentioned earlier for the forward leaning, the direct use of the root trajectory to estimate this force may lead to unnatural lateral leaning. Thus, the oscillatory details are filtered out from the trajectory as in . From the resulting trajectory, the moving speed and the radius of curvature are computed as follows:

v (t_{i}) = {({\dot{x}}_{i}^{2} + {\dot{y}}_{i}^{2})}^{1 / 2} and r (t_{i}) = \frac{{({\dot{x}}_{i}^{2} + {\dot{y}}_{i}^{2})}^{3 / 2}}{| {\dot{x}}_{i} ÿ_{i} - {\dot{y}}_{i} {\ddot{x}}_{i} |},

where

{[{\overset{x}{true}}_{i} {\overset{y}{true}}_{i}]}^{sans-serif T}

and

{[{\overset{x}{true}}_{i} ÿ_{i}]}^{sans-serif T}

are the first and second derivatives of the trajectory with respect to time, respectively.…”

Section: Automatic Motion Transformationmentioning

confidence: 99%

See 1 more Smart Citation

Online real‐time locomotive motion transformation based on biomechanical observations

Han

Hong

Noh

et al. 2016

Computer Animation & Virtual

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In the paper, we present an online real-time method for automatically transforming a basic locomotive motion to a desired motion of the same type, based on biomechanical results. Given an online request for a motion of a certain type with desired moving speed and turning angle, our method first extracts a basic motion of the same type from a motion graph, and then transforms it to achieve the desired moving speed and turning angle by exploiting the following biomechanical observations: contact-driven center-of-mass control, anticipatory reorientation of upper body segments, moving speed adjustment, and whole-body leaning. Exploiting these observations, we propose a simple but effective method to add physical and behavioral naturalness to the resulting locomotive motions without preprocessing. Through experiments, we show that our method enables a character to respond agilely to online user commands while efficiently generating walking, jogging, and running motions with a compact motion library. Our method can also deal with certain dynamical motions such as forward roll.

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“…In this process, we mainly focus on capturing the succession starting from the root and passing throughout the upper body parts. The timing changes for the lower body parts can be adjusted by skipping or delaying the time spent to lift or to lower a leg; however, these can potentially destroy some of the key physical characteristics and constraints embedded in the original motion [5], [19]. On the other hand, the spatial and temporal attributes of a motion in the upper body are less constrained and convey more stylistic variation for expressive locomotion [18].…”

Section: Upper-body Timing Transfermentioning

confidence: 99%

Temporal Transfer of Locomotion Style

Kim¹,

Kim²,

Neff³

2015

ETRI J

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Timing plays a key role in expressing the qualitative aspects of a character's motion; that is, conveying emotional state, personality, and character role, all potentially without changing spatial positions. Temporal editing of locomotion style is particularly difficult for a novice animator since observers are not well attuned to the sense of weight and energy displayed through motion timing; and the interface for adjusting timing is far less intuitive to use than that for adjusting pose. In this paper, we propose an editing system that effectively captures the timing variations in an example locomotion set and utilizes them for style transfer from one motion to another via both global and upper‐body timing transfers. The global timing transfer focuses on matching the input motion to the body speed of the selected example motion, while the upper‐body timing transfer propagates the sense of movement flow — succession — through the torso and arms. Our transfer process is based on key times detected from the example set and transferring the relative changes of angle rotation in the upper body joints from a timing source to an input target motion. We demonstrate that our approach is practical in an interactive application such that a set of short locomotion cycles can be applied to generate a longer sequence with continuously varied timings.

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Biomechanically-inspired motion path editing

Cited by 16 publications

References 27 publications

Detection and Synthesis of Full‐Body Environment Interactions for Virtual Humans

Detection and Synthesis of Full‐Body Environment Interactions for Virtual Humans

Online real‐time locomotive motion transformation based on biomechanical observations

Temporal Transfer of Locomotion Style

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