Modeling BharataNatyam dance steps: art to SMart
03 Sep 2012-pp 320-325
TL;DR: A computational model to represent BN dance steps is proposed as a SMart system for modelling BN steps, where SMart stands for System Modelled art and the detailed description of formulation of a dance position vector that comprises of thirty explicitly identified attributes is presented.
Abstract: BharataNatyam (BN) like any other Indian classical dance comprises of a sequence of possible and legitimate dance steps. It is estimated that using the main body parts namely head, neck, hand and leg itself, more than 5 lakh dance steps can be generated for a single beat. Choreographers and even dancers usually repeat their favorite dance steps or the conventional casual dance steps taught by their teacher while performing for multiple beats. As a result several valid and many other significant non-traditional dance steps remain unexplored. Hence, we propose to have an auto enumeration followed by auto classification of significant BN dance steps that can be used in dance performance and choreography. In short, we try to transform sheer art into a System Modelled art i.e. 'Art to SMart'. The foremost and most challenging task is to have a computational model that represents different BN dance poses. In this paper, we have proposed a computational model to represent BN dance steps and have presented the detailed description of formulation of a dance position vector that comprises of thirty explicitly identified attributes to capture and represent all variations of a BN dance step. We have named it as a SMart system for modelling BN steps, where SMart stands for System Modelled art. We have also demonstrated sample dance steps and their corresponding representations with appropriate dance step images.
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01 Dec 2012
TL;DR: This paper discusses an automated approach to obtain unexplored dance steps using a proposed fitness function for a single beat/count and incorporates certain measures to ensure that the proposed dance steps should be feasible and appropriate.
Abstract: Dance choreography is an intense, creative and intuitive process. A choreographer has to finalize appropriate dance steps from amongst millions of possibilities. Though it is not impossible, the choreographer being human cannot explore, analyze and remember all these variations among steps due to large scale of available options. Hence, we propose to simplify the problem of exploring and selecting dance steps from amongst the huge set of all possible variations for an Indian Classical Dance, BharataNatyam (BN). Based on a computational model developed by Jadhav et al. [13], we propose a Genetic Algorithm (GA) driven automatic system that would provide a list of unexplored novel dance steps to choreographers. We have incorporated certain measures to ensure that the proposed dance steps should be feasible and appropriate. In this paper, we discuss an automated approach to obtain unexplored dance steps using a proposed fitness function for a single beat/count. The details of experimental study performed for the Genetic Algorithm based art to SMart (System Modelled art) system along with the results obtained are also presented in this paper.
15 citations
Cites methods from "Modeling BharataNatyam dance steps:..."
...We represent each BN dance step using 30 explicitly derived attributes which capture six important major limbs (head, hands right and left, waist, right and left leg) [13] A dance step is represented as a dance vector....
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...We have represented these dance steps using our dance step representation vector model in [13]....
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...[13], we propose a Genetic Algorithm (GA) driven automatic system that would provide a list of unexplored novel dance steps to choreographers....
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TL;DR: This work proposes to develop an autoenumeration followed by autoclassification of significant BN dance steps that can be used in dance performance and choreography, and designed Art to SMart as a system to model the dance art of BharataNatyam.
Abstract: Hence, we propose to develop an autoenumeration followed by autoclassification of significant BN dance steps that can be used in dance performance and choreography. The foremost and most challenging task is to have a computational model that represents different BN dance poses. The second task is to develop a genetic algorithm GA-driven automatic system that would provide choreographers a list of unexplored, novel dance steps to fit in a single beat. We designed Art to SMart as a system to model the dance art of BharataNatyam. This system generates dance poses. Furthermore, we have developed a stick figure generation module to help visualize the 30-attribute dance vector generated from the system. The results are evaluated using a mean opinion score measure.
11 citations
Posted Content•
TL;DR: This paper is an attempt to review research work reported in the literature, categorize and group significant research work completed in a span of 1967–2020 in the field of automating dance, and identify six major categories corresponding to the use of computers in dance automation.
Abstract: Dance is an art and when technology meets this kind of art, it's a novel attempt in itself. Several researchers have attempted to automate several aspects of dance, right from dance notation to choreography. Furthermore, we have encountered several applications of dance automation like e-learning, heritage preservation, etc. Despite several attempts by researchers for more than two decades in various styles of dance all round the world, we found a review paper that portrays the research status in this area dating to 1990 \cite{politis1990computers}. Hence, we decide to come up with a comprehensive review article that showcases several aspects of dance automation.
This paper is an attempt to review research work reported in the literature, categorize and group all research work completed so far in the field of automating dance. We have explicitly identified six major categories corresponding to the use of computers in dance automation namely dance representation, dance capturing, dance semantics, dance generation, dance processing approaches and applications of dance automation systems. We classified several research papers under these categories according to their research approach and functionality. With the help of proposed categories and subcategories one can easily determine the state of research and the new avenues left for exploration in the field of dance automation.
7 citations
Cites methods from "Modeling BharataNatyam dance steps:..."
...[40] have used vector space for modelling BharataNatyam....
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...[60] have automated the process of twodimensional Stick Figure generation from the 30-attribute human body model [40]....
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10 Oct 2014
TL;DR: This paper has developed a model to represent a BN dance step through a unique thirty attribute dance position vector and presents the details of stick figure and in particular how to mold it to suit to a Bn dance pose that corresponds to a Dance position vector.
Abstract: BharataNatyam (BN) is an ancient Indian Classical Dance dating centuries ago This unique classical dance has been taught by a teacher to a student mostly by rote learning method A student uses various methods to record the dance choreography taught by a teacher Currently recording through mobile phones and various other devices of a live dance performance are popular methods but it has its own inherent disadvantagesSeveral attempts of automation in choreography are reported and dance visualization is the key factor Stick Figure representation is a popular method amongst all and is still being used by many of the practitionersWe have developed a model to represent a BN dance step through a unique thirty attribute dance position vector Evolutionary approach of Genetic Algorithms generates non-conventional BN dance poses which are approved by renowned dance experts However, in order to visualize every resulting BN dance, a human model has to pose accordingly We overcame this hurdle by developing a stick figure generation module In this paper we present the details of stick figure and in particular how we mold it to suit to a BN dance pose that corresponds to a Dance position vector
7 citations
Cites background from "Modeling BharataNatyam dance steps:..."
...The meaning of each of these attributes is concisely and intricately explained in Jadhav et al.[2]....
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...As per the BharataNatyam coding framework proposed by Jadhav et al. [2], the bits are distributed to each of the modules....
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...The meaning of each of these attributes is concisely and intricately explained in Jadhav et al.[2]....
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...[2], the bits are distributed to each of the modules....
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...The ancient art of ICD has been well documented in these ancient manuscripts and we have successfully modeled the major limbs of the body [2] using this....
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01 Jan 2014
TL;DR: This paper presents the experimental ArtToSMart (System Modeled art) system, which gradually enhanced from one beat dance pose generation to ‘m’ beat dance sequences generation comprising of ‘n’ system generated dance poses and a machine learning model to classify system generated BN sequences.
Abstract: BharataNatyam (BN) is an ancient Indian Classical Dance (ICD). Creativity and innovation are the soul of any art including BN dance. Within the framework of rules and traditionally accepted boundaries, choreographers try to innovate and create unseen, aesthetic and novel BN dance sequences. The human efforts can be supported with the computational assistance to generate valid, genuine BN dance sequences. Moreover, these movements can be empowered by the unspecified rules extracted from the analysis of “Adavus”, which are considered ideal BN dance sequences for “Nritta” or pure dance movements. Thus an altogether new and interesting sequence can be obtained. In this paper we present our experimental ArtToSMart (System Modeled art) system, which gradually enhanced from one beat dance pose generation to ‘m’ beat dance sequences generation comprising of ‘m’ system generated dance poses. Furthermore, we are tagging these sequences with the help of BN dance experts and trying to develop a machine learning model to classify system generated BN sequences. The use of Rough Set tools have proved to be impressive for the same.
6 citations
References
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TL;DR: The efficacy of the ontology-based approach is demonstrated by constructing an ontology for the cultural heritage domain of Indian classical dance, and a browsing application is developed for semantic access to the heritage collection of Indian dance videos.
Abstract: Preservation of intangible cultural heritage, such as music and dance, requires encoding of background knowledge together with digitized records of the performances. We present an ontology-based approach for designing a cultural heritage repository for that purpose. Since dance and music are recorded in multimedia format, we use Multimedia Web Ontology Language (MOWL) to encode the domain knowledge. We propose an architectural framework that includes a method to construct the ontology with a labeled set of training data and use of the ontology to automatically annotate new instances of digital heritage artifacts. The annotations enable creation of a semantic navigation environment in a cultural heritage repository. We have demonstrated the efficacy of our approach by constructing an ontology for the cultural heritage domain of Indian classical dance, and have developed a browsing application for semantic access to the heritage collection of Indian dance videos.
66 citations
"Modeling BharataNatyam dance steps:..." refers background in this paper
...The combinations are constrained by physical constraints, aesthetic constraints, preferential constraints, etc. Treatises like Bharatamuni s Natyasastra [1] and Nandikeshwara s Abhinayadarpana [1] codify such constraints and rules, based on human experience and existing literature....
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...For every body part, there exist several movements which are well defined and documented in the ancient scriptures [1]....
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...RELATED WORK The work carried out till date for dance can be broadly classified under the following main heads – Animation[17],[18], Ontology based construction of Indian Classical Dance repository[1][2], Motion capture[6][7][11][14][19], extracting dance’s semantic content [4][5][12][15]....
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...Table 1 below lists the main body part movements and the possible ways of doing them as mentioned in Natyasastra [1]....
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...Table 1 below lists the main body part movements and the possible ways of doing them as mentioned in Natyasastra [1]....
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TL;DR: A method is proposed which attempts to capture the semantic similarities between the database items by modelling audio, artist names, and tags in a single low-dimensional semantic embedding space by optimizing the set of prediction tasks of interest jointly using multi-task learning.
Abstract: Music prediction tasks range from predicting tags given a song or clip of audio, predicting the name of the artist, or predicting related songs given a song, clip, artist name or tag. That is, we are interested in every semantic relationship between the different musical concepts in our database. In realistically sized databases, the number of songs is measured in the hundreds of thousands or more, and the number of artists in the tens of thousands or more, providing a considerable challenge to standard machine learning techniques. In this work, we propose a method that scales to such datasets which attempts to capture the semantic similarities between the database items by modelling audio, artist names, and tags in a single low-dimensional semantic embedding space. This choice of space is learnt by optimizing the set of prediction tasks of interest jointly using multi-task learning. Our single model learnt by training on the joint objective function is shown experimentally to have improved accur...
48 citations
"Modeling BharataNatyam dance steps:..." refers methods in this paper
...Proposing a method that scales to datasets which attempts to capture the semantic similarities between the database items by modeling audio, artist names, and tags in a single lowdimensional semantic embedding space can be seen in [10]....
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TL;DR: It is shown that PCE improves pose estimation accuracy over estimating each person independently, and it learns meaningful prototypes which can be used as priors for pose estimation in novel images.
Abstract: Most existing techniques for articulated Human Pose Estimation (HPE) consider each person independently. Here we tackle the problem in a new setting, coined Human Pose Coestimation (PCE), where multiple people are in a common, but unknown pose. The task of PCE is to estimate their poses jointly and to produce prototypes characterizing the shared pose. Since the poses of the individual people should be similar to the prototype, PCE has less freedom compared to estimating each pose independently, which simplifies the problem. We demonstrate our PCE technique on two applications. The first is estimating the pose of people performing the same activity synchronously, such as during aerobics, cheerleading, and dancing in a group. We show that PCE improves pose estimation accuracy over estimating each person independently. The second application is learning prototype poses characterizing a pose class directly from an image search engine queried by the class name (e.g., “lotus pose”). We show that PCE leads to better pose estimation in such images, and it learns meaningful prototypes which can be used as priors for pose estimation in novel images.
40 citations
"Modeling BharataNatyam dance steps:..." refers background in this paper
...RELATED WORK The work carried out till date for dance can be broadly classified under the following main heads – Animation[17],[18], Ontology based construction of Indian Classical Dance repository[1][2], Motion capture[6][7][11][14][19], extracting dance’s semantic content [4][5][12][15]....
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...Eichner et al in [14] presented the novel human Pose Co-Estimation technique (PCE) for joint pose estimation over multiple persons in a common, but for unknown pose....
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27 Jun 2011
TL;DR: A new and simple two-level decision making system has been designed for performing scale-, translation- and rotation-invariant recognition of various single-hand gestures of a dancer in Bharatanatyam, an Indian classical dance form.
Abstract: A new and simple two-level decision making system has been designed for performing scale-, translation- and rotation-invariant recognition of various single-hand gestures of a dancer. The orientation filter is used at the first-level to generate a feature vector that is able to distinguish between several gestures. At the second-level the silhouette of the different gestures is extracted, followed by the generation of the corresponding skeleton and the evaluation of the gradients at its end points. These gradients constitute the second feature set, for recognizing those gestures which remain to be identified at the first-level. An application has been provided in the domain of single-hand gestures of Bharatanatyam, an Indian classical dance form.
35 citations
Dissertation•
01 Jan 1993
TL;DR: In this article, the authors examined Merce Cunningham's making of the dance Trackers, which was created with the assistance of LifeForms, a computer choreographic software tool developed at Simon Fraser University in the computer graphics research lab.
Abstract: The thesis examines Merce Cunningham’s making of the dance Trackers, which was created with the assistance of LifeForms, a computer choreographic software tool developed at Simon Fraser University in the computer graphics research lab. LifeForms provides an interactive, graphical interface that enables a choreographer to sketch out movement ideas in space and time. The making of Trackers is an example of an ongoing process of exploration in Merce Cunningham’s work with computer technology. Trackers was premiered on Tuesday, March 19, 1991, at the New York City Center Theater, and was performed by Merce Cunningham and eleven of the Cunningham Company dancers. The author, a member of the LifeForms design team at Simon Fraser University, has worked with Cunningham since December 1989 for a period totaling seventy five days, tutoring him in the use of LifeForms and supporting his creation of new movement. The history of dance and the use of computers is examined in order to provide technological context and supporting material to the case study of Cunningham’s making of Trackers. Choreographic process as it occurs in the studio is explored, in order to assess how computer technology can be used as a supportive tool in the creation, recording and interpretation of dance. This provides a background to the description of computers systems that have been developed for dance.
30 citations
"Modeling BharataNatyam dance steps:..." refers background or methods in this paper
...RELATED WORK The work carried out till date for dance can be broadly classified under the following main heads Animation[17],[18], Ontology based construction of Indian Classical Dance repository[1][2], Motion capture[6][7][11][14][19], extracting dance s semantic content [4][5][12][15]....
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...RELATED WORK The work carried out till date for dance can be broadly classified under the following main heads – Animation[17],[18], Ontology based construction of Indian Classical Dance repository[1][2], Motion capture[6][7][11][14][19], extracting dance’s semantic content [4][5][12][15]....
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...How movement phrases were created using choreographic software LifeForms and how using the software affected his choreographic methods, and most importantly how LifeForms enabled him to extend his imagination and expand possibilities for creating movement can be seen in [18]....
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