Understanding visuo‐motor primitives for motion synthesis and analysis

Guerra-Filho, Gutemberg; Aloimonos, Yiannis

doi:10.1002/cav.124

Cited by 21 publications

(12 citation statements)

References 16 publications

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“…Some kinematic features are joint positions relative to other points or planes [Jenkins and Matarić 2003;Jenkins and Matarić 2004;Müller et al 2005;Peng 2010;Reng et al 2006], linear or angular velocity [Bernhardt and Robinson 2007;Fod et al 2002;Ilg et al 2004;Kwon and Shin 2005;Mezger et al 2005;Mori and Uehara 2001;Osaki et al 2000;Shiratori et al 2003], linear or angular acceleration [Bindiganavale and Badler 1998;Guerra-Filho and Aloimonos 2006], and curvature [Zhao and Badler 2005;Wang et al 2001]. Some changes in these kinematic features that are used to determine segment boundaries are local minima or maxima, zero-crossings, and thresholds.…”

Section: Related Workmentioning

confidence: 99%

Segmenting motion capture data using a qualitative analysis

Bouchard

Badler

2015

Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games

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Many interactive 3D games utilize motion capture for both character animation and user input. These applications require short, meaningful sequences of data. Manually producing these segments of motion capture data is a laborious, time-consuming process that is impractical for real-time applications. We present a method to automatically produce semantic segmentations of general motion capture data by examining the qualitative properties that are intrinsic to all motions, using Laban Movement Analysis (LMA). LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and lowlevel kinematic features, which often yield unsophisticated segmentations. Our method finds motion sequences which exhibit high output similarity from a collection of neural networks trained with temporal variance. We show that segmentations produced using LMA features are more similar to manual segmentations, both at the frame and the segment level, than several other automatic segmentation methods. University of Pennsylvania AbstractMany interactive 3D games utilize motion capture for both character animation and user input. These applications require short, meaningful sequences of data. Manually producing these segments of motion capture data is a laborious, time-consuming process that is impractical for real-time applications. We present a method to automatically produce semantic segmentations of general motion capture data by examining the qualitative properties that are intrinsic to all motions, using Laban Movement Analysis (LMA). LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and lowlevel kinematic features, which often yield unsophisticated segmentations. Our method finds motion sequences which exhibit high output similarity from a collection of neural networks trained with temporal variance. We show that segmentations produced using LMA features are more similar to manual segmentations, both at the frame and the segment level, than several other automatic segmentation methods.

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Section: Related Workmentioning

confidence: 99%

Segmenting motion capture data using a qualitative analysis

Bouchard

Badler

2015

Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games

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“…Rui and Anandan [23] used principal components of frame-toframe optical-flow to discover temporal trajectories of human motion in video. Recently, Guerra-Filho and Aloimonos [9], [10] presented a linguistic framework for modeling and learning of human activity representations. The low level representation of their framework, motion primitives, referred to as kinetemes, are studied as the foundation for a kinetic language.…”

Section: Previous Workmentioning

confidence: 99%

“…Research that addresses the problem of detection, recognition and synthesis of human human motion have gained substantial interests from both academia and industry over the last few years due to the large number of applications [1], [20], [13], [15], [22]. Unsupervised techniques for learning motion primitives from data have recently drawn the interest of many scientists in computer vision [9], [28], [27], [17] and computer graphics [4], [16], [3], [8]. Although previous research has shown promising results, recognizing human activities and factorizing human motion into primitives and actions (i.e.…”

Section: Introductionmentioning

confidence: 99%

Temporal segmentation and activity classification from first-person sensing

Spriggs

Torre

Hebert

2009

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

204

158

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Temporal segmentation of human motion into actions is central to the understanding and building of computational models of human motion and activity recognition. Several issues contribute to the challenge of temporal segmentation and classification of human motion. These include the large variability in the temporal scale and periodicity of human actions, the complexity of representing articulated motion, and the exponential nature of all possible movement combinations. We provide initial results from investigating two distinct problems -classification of the overall task being performed, and the more difficult problem of classifying individual frames over time into specific actions. We explore first-person sensing through a wearable camera and Inertial Measurement Units (IMUs) for temporally segmenting human motion into actions and performing activity classification in the context of cooking and recipe preparation in a natural environment. We present baseline results for supervised and unsupervised temporal segmentation, and recipe recognition in the CMU-Multimodal activity database (CMU-MMAC).

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“…The quality of the detection, recognition, or synthesis in these applications greatly depends on the spatial and temporal resolution of motion databases, as well as the complexity of the models. Unsupervised techniques to learn motion primitives from training data have recently attracted the interest of many scientists in computer vision [4], [5], [6], [7], [8], [9], [10], [11], [12] and computer graphics [13], [14], [15], [16], [17], [18], [19], [20]. Fig.…”

mentioning

confidence: 99%

“…Over the last few years, several approaches for unsupervised segmentation of activities have been proposed (see, for example, [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18]). HACA presents several advantages:…”

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confidence: 99%

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Zhou

Torre

Hodgins

2013

IEEE Trans. Pattern Anal. Mach. Intell.

306

186

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Abstract-Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as a temporal clustering one, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for the time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

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Understanding visuo‐motor primitives for motion synthesis and analysis

Cited by 21 publications

References 16 publications

Segmenting motion capture data using a qualitative analysis

Segmenting motion capture data using a qualitative analysis

Temporal segmentation and activity classification from first-person sensing

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

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