Motion Key-frames extraction based on amplitude of distance characteristic curve

Zhang, Qiang; Xue, Xiang; Zhou, Dongsheng; Wei, Xiaopeng

doi:10.1080/18756891.2013.859873

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…The algorithm by Yang et al [13] applies Butterworth filtering and PCA to the input data and then selects keyframes with zero-crossing points of velocity. Zhang et al's method [14] creates motion curves from the amplitudes of motion of joints, applies PCA, defines a distance characteristic curve, and eventually uses this curve to extract keyframes. The method proposed by Halit and Capin [15] defined a metric named 'motion saliency'.…”

Section: Related Workmentioning

confidence: 99%

Keyframe Extraction Using Linear Rotation Invariant Coordinates

MUTLU

Çelikcan

2022

Sakarya University Journal of Science

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Keyframe extraction is a widely applied remedy for issues faced with 3D motion capture -based computer animation. In this paper, we propose a novel keyframe extraction method, where the motion is represented in LRI coordinates and the dimensions covering 95% of the data are automatically selected using PCA. Then, by K-means classification, the summarized data is clustered and a keyframe is extracted from each cluster based on cosine similarity. To validate the method, an online user study was conducted. The results show that 45% of the participants preferred the keyframes extracted using the proposed method, outperforming the alternative by 6%.

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Section: Related Workmentioning

confidence: 99%

Keyframe Extraction Using Linear Rotation Invariant Coordinates

MUTLU

Çelikcan

2022

Sakarya University Journal of Science

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“…Peng [9] proposed a method based on the center distance to extract the keyframe. Zhang et al [10] extracted the keyframe based on the joint distance by using nine joint features to represent the original motion sequences, before applying PCA to reduce the dimensionality of the data and obtain one-dimensional curves. The extreme curve was then extracted as the initial keyframe point and the final keyframe interpolation procedure was based on the threshold between the extreme points.…”

Section: Open Accessmentioning

confidence: 99%

“…The Euler angle is used to interpolate the rotation. The average inter-frame distance between the original motion sequence and the motion sequence reconstruction is selected as the reconstruction error [10]:…”

Section: Calculation Of the Motion Sequence Reconstruction Errormentioning

confidence: 99%

Keyframe Extraction from Human Motion Capture Data Based on a Multiple Population Genetic Algorithm

Zhang

Zhou

2014

Symmetry

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Abstract:To reduce reconstruction errors during keyframe extraction and to control the optimal compression ratio, this study proposes a method for keyframe extraction from human motion capture data based on a multiple population genetic algorithm. The fitness function is defined to meet the goals of minimal reconstruction errors and the optimal compression rate, where multiple initial populations are subjected to co-evolution. The multiple population genetic algorithm considers global and local search. Experimental results showed that the algorithm can effectively extract the keyframe from motion capture data and it satisfied the desired reconstruction error.

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“…• One of the approaches is to detect the occurrence of a feature in the motion, in which poses that contain the feature are considered as keyframes [2,66,18,12,28,44,54,55,79].…”

Section: Related Work and Technical Challengesmentioning

confidence: 99%

“…• Detection, the process of selecting keyframes by detecting the occurrence of features or patterns within the poses or curves of the animation. We categorize the following research as detection-based: [2,66,18,12,28,44,54,55,79].…”

Section: Keyframe Selectionmentioning

confidence: 99%

Converting Motion Capture Into Editable Keyframe Animation: Fast, Optimal, and Generic Keyframe Selection

Roberts

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<p>Motion capture is attractive to visual effects studios because it offers a fast and automatic way to create animation directly from actors' movements. Despite extensive research efforts toward motion capture processing and motion editing, animations created using motion capture are notoriously difficult to edit. We investigate this problem and develop a technique to reverse engineer editable keyframe animation from motion capture. Our technique for converting motion capture into editable animation is to select keyframes from the motion capture that correspond to those an animator might have used to create the motion from scratch. As the first contribution presented by this thesis, we survey both traditional and contemporary animation practice to define the types of keyframes created by animators following conventional animation practices. As the second contribution, we develop a new keyframe selection algorithm that uses a generic objective function; using different implementations, we can define different criteria to which keyframes are selected. After presenting the algorithm, we return to the problem of converting motion capture into editable animation and design three implementations of the objective function that can be used together to select animator-like keyframes. Finally, as a minor contribution to conclude the thesis, we present a simple interpolation algorithm that can be used to construct a new animation from only the selected keyframes. In contrast to previous research in the topic of keyframe selection, our technique is novel in that we have designed it to provide selections of keyframes that are similar in structure to those used by animators following conventional practices. Consequently, both animators and motion editors can adjust the resulting animation in much the same way as their own, manually created, content. Furthermore, our technique offers an optimal guarantee paired with fast performance for practical editing situations, which has not yet been achieved in previous research. In conclusion, the contributions of this thesis advance the state of the art in the topic by introducing the first fast, optimal, and generic keyframe selection algorithm. Ultimately, our technique is not only well suited to the problem of recovering editable animation from motion capture, but can also be used to select keyframes for other purposes - such as compression or pattern identification - provided that an appropriate implementation of the objective function can be imagined and employed.</p>

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Motion Key-frames extraction based on amplitude of distance characteristic curve

Cited by 8 publications

References 13 publications

Keyframe Extraction Using Linear Rotation Invariant Coordinates

Keyframe Extraction Using Linear Rotation Invariant Coordinates

Keyframe Extraction from Human Motion Capture Data Based on a Multiple Population Genetic Algorithm

Converting Motion Capture Into Editable Keyframe Animation: Fast, Optimal, and Generic Keyframe Selection

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