Motion capture assisted animation

Pullen, Katherine; Bregler, Christoph

doi:10.1145/566570.566608

Cited by 99 publications

(34 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several researchers have observed that there is a lot of redundancy in human motion,1 which is caused by the fact that human joints act in a coordinated manner for any kind of motion 11. Various animation methods, covering a wide range of techniques from inverse kinematics to procedural animation, make use of this property.…”

Section: Related Workmentioning

confidence: 99%

Multiscale motion saliency for keyframe extraction from motion capture sequences

Halit

Çapın

2010

Computer Animation & Virtual

View full text Add to dashboard Cite

Motion capture is an increasingly popular animation technique; however data acquired by motion capture can become substantial. This makes it difficult to use motion capture data in a number of applications, such as motion editing, motion understanding, automatic motion summarization, motion thumbnail generation, or motion database search and retrieval. To overcome this limitation, we propose an automatic approach to extract keyframes from a motion capture sequence. We treat the input sequence as motion curves, and obtain the most salient parts of these curves using a new proposed metric, called 'motion saliency'. We select the curves to be analysed by a dimension reduction technique, Principal Component Analysis (PCA). We then apply frame reduction techniques to extract the most important frames as keyframes of the motion. With this approach, around 8% of the frames are selected to be keyframes for motion capture sequences.

show abstract

Section: Related Workmentioning

confidence: 99%

Multiscale motion saliency for keyframe extraction from motion capture sequences

Halit

Çapın

2010

Computer Animation & Virtual

View full text Add to dashboard Cite

show abstract

“…Similar to multi-resolution representations of texture [10] and movie texture [2], Pullen and Bregler modeled cyclic motions by multiresolution signals. The term motion texture was originally used by Pullen and Bregler (and suggested by Perlin) as their project name [34]. Our motion texture model is completely different from theirs.…”

Section: Related Workmentioning

confidence: 99%

Motion texture

2002

View full text Add to dashboard Cite

Figure 1: This 320-frame sequence of dance motion is choreographed from (1) the starting frame, (2) the ending frame and (3) the learnt motion texture from motion captured dance data. Four motion textons are generated from the motion texture and then used to synthesize all the frames in this sequence. A number of key frames are also shown in the figure to demonstrate that the synthesized motion is natural, smooth and realistic (Two red lines indicate the trajectories of the right hand and right foot). AbstractIn this paper, we describe a novel technique, called motion texture, for synthesizing complex human-figure motion (e.g., dancing) that is statistically similar to the original motion captured data. We define motion texture as a set of motion textons and their distribution, which characterize the stochastic and dynamic nature of the captured motion. Specifically, a motion texton is modeled by a linear dynamic system (LDS) while the texton distribution is represented by a transition matrix indicating how likely each texton is switched to another. We have designed a maximum likelihood algorithm to learn the motion textons and their relationship from the captured dance motion. The learnt motion texture can then be used to generate new animations automatically and/or edit animation sequences interactively. Most interestingly, motion texture can be manipulated at different levels, either by changing the fine details of a specific motion at the texton level or by designing a new choreography at the distribution level. Our approach is demonstrated by many synthesized sequences of visually compelling dance motion.

show abstract

“…Arikan and Forsyth [8] created a hierarchy of graphs and employed a randomized search algorithm for the synthesis of a new motion subject to user-specified constraints. Pullen and Bregler [12] segmented motion data into small pieces and rearranged them to match user-specified keyframes. In 2003, Arikan and his colleagues [9] presented a new search approach based on dynamic programming that supports user-specified annotations of the motion.…”

Section: Introductionmentioning

confidence: 99%

Synthesizing Human Motion from Intuitive Constraints

Safonova

Hodgins

2009

Artificial Intelligence Techniques for Computer Graphics

View full text Add to dashboard Cite

Many compelling applications would become feasible if novice users had the ability to synthesize high quality human motion based only on a simple sketch and a few easily specified constraints. Motion graphs and their variations have proven to be a powerful tool for synthesizing human motion when only a rough sketch is given. Motion graphs are simple to implement, and the synthesis can be fully automatic. When unrolled into the environment, motion graphs, however, grow drastically in size. The major challenge is then searching these large graphs for motions that satisfy user constraints. A number of sub-optimal algorithms that do not provide guarantees on the optimality of the solution have been proposed. In this paper, we argue that in many situations to get natural results an optimal or nearly-optimal search is required. We show how to use the wellknown A* search to find solutions that are optimal or of bounded sub-optimality. We achieve this goal for large motion graphs by performing a lossless compression of the motion graph and implementing a heuristic function that significantly accelerates the search for the domain of human motion. We demonstrate the power of this approach by synthesizing optimal or near optimal motions that include a variety of behaviors in a single motion. These experiments show that motions become more natural as the optimality improves.

show abstract

Motion capture assisted animation

Cited by 99 publications

References 26 publications

Multiscale motion saliency for keyframe extraction from motion capture sequences

Multiscale motion saliency for keyframe extraction from motion capture sequences

Motion texture

Synthesizing Human Motion from Intuitive Constraints

Contact Info

Product

Resources

About