A Coordinate-Invariant Approach to Multiresolution Motion Analysis

Lee, Jehee

doi:10.1006/gmod.2001.0548

Cited by 33 publications

(25 citation statements)

References 23 publications

(28 reference statements)

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“…To smooth both position and orientation in a coherent way, we employ the general filtering scheme for motion data [Lee and Shin 2001;. In particular, we adopt a binomial filter mask ( …”

Section: Footprint Refiningmentioning

confidence: 99%

Planning biped locomotion using motion capture data and probabilistic roadmaps

2003

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Typical high-level directives for locomotion of human-like characters are useful for interactive games and simulations as well as for off-line production animation. In this paper, we present a new scheme for planning natural-looking locomotion of a biped figure to facilitate rapid motion prototyping and task-level motion generation. Given start and goal positions in a virtual environment, our scheme gives a sequence of motions to move from the start to the goal using a set of live-captured motion clips. Based on a novel combination of probabilistic path planning and hierarchical displacement mapping, our scheme consists of three parts: roadmap construction, roadmap search, and motion generation. We randomly sample a set of valid footholds of the biped figure from the environment to construct a directed graph, called a roadmap, that guides the locomotion of the figure. Every edge of the roadmap is associated with a live-captured motion clip. Augmenting the roadmap with a posture transition graph, we traverse it to obtain the sequence of input motion clips and that of target footprints. We finally adapt the motion sequence to the constraints specified by the footprint sequence to generate a desired locomotion.

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“…To smooth both position and orientation in a coherent way, we employ the general filtering scheme for motion data [Lee and Shin 2001;. In particular, we adopt a binomial filter mask ( …”

Section: Footprint Refiningmentioning

confidence: 99%

Planning biped locomotion using motion capture data and probabilistic roadmaps

2003

Self Cite

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“…Exaggerating joint angles can cause unexpected results due to singularities [23], but our method does not suffer from the singularity problem because it exaggerates the spatial joint trajectory. Moreover our method is as simple as other joint-angle exaggeration techniques [38,23,5,19].…”

Section: Introductionmentioning

confidence: 94%

“…Bruderlin and Williams [5] applied multiresolution analysis to each joint channel of a character motion and derived several new motion editing methods. Lee and Shin [23] proposed the rotation-invariant multiresolution analysis using quaternions, which do not suffer from the singularity problem of joint angles. More recently, Wang et al [38] have applied the Laplacian of a Gaussian filter to general animation signals to generate anticipation and follow-through effects.…”

Section: Related Workmentioning

confidence: 99%

Rubber-like Exaggeration for Character Animation

Meyer²

2007

15th Pacific Conference on Computer Graphics and Applications (PG'07)

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Motion capture cannot generate cartoon-style animation directly. We emulate the rubber-like exaggerations common in traditional character animation as a means of converting motion capture data into cartoon-like movement. We achieve this using trajectory-based motion exaggeration while allowing the violation of link-length constraints. We extend this technique to obtain smooth, rubber-like motion by dividing the original links into shorter sub-links and computing the positions of joints using Bézier curve interpolation and a mass-spring simulation. This method is fast enough to be used in real time.

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“…Several approaches have been proposed in this context. Well established are data-driven methods, which use a mocap database as the knowledge base [19][20][21][22][23][24] of how people move. Typically, the knowledge base is searched during the reconstruction process to find best matches according to the low dimensional input.…”

Section: Related Workmentioning

confidence: 99%