An Extensive Review of Computational Dance Automation Techniques and Applications

by   Manish Joshi, et al.

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 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.



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1 Introduction

Efforts of combining dance and computational power can be traced back to 1967. Being a domain that needs relatively more innovation and creativity than mere following standard procedures, dance was the slowest to adopt technology. The earliest attempt was published by A. Michael Noll [2] in Dance Magazine in 1967, although New York based choreographer Merce Cunningham also did the same [3].

In 1978, Savage et al. [4] presented an interactive computer model Choreo. Thereafter several research papers were presented that proposed and demonstrated the use of computers to refine as well as excel various aspects of dance with the advancements in technology. We have enlisted several dance aspects that are being worked upon. It includes dance representation, dance capturing, dance interpretation, dance generation etc. In this review paper we have presented details of the research work proposed for enhancement of various aspects of dance by various researchers. Our objective is to provide a comprehensive analysis of the research issues and efforts in the area of computer assisted dance automation. We propose to use a term ’dance informatics’ to refer research work in the area of computer assisted dance automation.

We have discussed several aspects of dance informatics as follows. Every dance style has its own notations to represent the dance. Researchers have come up with dance grammar concept too. We have discussed research work related with dance representation in section 2. The techniques to capture the dance and then process it further have changed along with time. Several research papers have presented different ideas and proposals to capture dance. All these research contributions are summarized in section 3. Section 4 presents research experiments that associate semantics with dance. Various approaches of dance interpretation using annotation, ontology etc. are presented in this section. Computer aided choreography is a keyword that corresponds to automation of the dance generation process.The features of Computer aided choreography are mentioned in greater details in section 5. Several approaches including probabilistic model, evolutionary model, classification, multi agent systems etc. are proposed by researchers to automate and process dance. A list of such different approaches used in dance informatics to enhance the dance is presented in section 6. Some recent efforts of dance visualization and dance robotics are also discussed separately in section 7 and section 8 respectively. The dance informatics research has certain real life applications. A list of such applications and use of dance informatics in specified applications are described in section 9, followed by conclusions in section 10.

2 Dance Representation

The foremost and essential step in dance automation is dance Representation. In order to comprehend and communicate dance steps correctly, a few dance representation techniques are already exist.

Just like music notations, which are considered as standards for storing and playing it across various platforms, it is essential to represent dance standards. Various methods are used to represent dance. Computational power is utilized to represent dance by developing systems that can work around dance notations. Dance representation can be accomplished using dance notations as well as using dance grammar. Figure 1 outlines both of these aspects of dance representation and the research related to these aspects is described in subsequent sections.

Figure 1: Dance Representation

2.1 Notation

The primary use of Dance notations is the preservation of classical dance documentation. It can be further used for analysis and reconstruction of choreography and dance forms or technical exercises. Many different forms of dance notations have been created but the two main systems used in Western culture are Labanotation (also known as Kinetography Laban) and Benesh Movement Notation [5]. Eshkol-Wachman Movement Notation and DanceWriting are also in use, but to a lesser extent [5]. A lot of research work can be noticed dedicated especially in the area of dance notation and its standardization especially, Labanotation followed by Latin American dance styles and then Indian Classical dance. Latin American dance styles like Ballroom, Foxtrot, Waltz has been used successfully for several research attempts while Indian classical dances like BharataNatyam, Kutchipudi, Oddisi, etc. have relatively fewer research attempts.

Dance Notation Bureau in New York has been working for more than six decades to disseminate dance scores recorded with this system [6]. The resulting archive provides scholars, dancers, students, performers and the public with an easily accessible detailed record of choreography that allows dances to be studied.

2.1.1 Labanotation

A noted Russian BharataNatyam dancer, Annemette Karpen in her paper [7] has tried to solve the problem of notation for BharataNatyam by using Labanotation. She has stated that video recordings are not a good method of presenting dance movements since camera catches only one angle that too from the eyes of the director and not the finest details. She also stated that Sutton Dance writing is an easy method for notating dance but available only for western dances and so she tried using the same for BharataNatyam and realized its limitations in not being able to capture the finer movements of BharataNatyam’s facial expressions, hand gestures, neck, shoulder movements and intricate footwork. So she also tried the same using Labanotation which is again used for Western dances only and had similar problems as above. Ebenreuter [8] has attempted to design an interface to facilitate the exact documentation of dance notation while the LabanDancer system developed by Wilke et al. [9] helped to translate the recorded Labanotation scores into 3-D human figure animations. There are few dancers or choreographers who can read notation or even capable of producing scores. Thus a computer based tool to animate the notation would prove very helpful. Labanwriter is used for creating and editing dance scores. LifeForms [10] is a product of Credo Interactive for 3D choreography, animation and motion picture which helps to experiment with patterns of movement in animated human figure for Labanotation.

2.1.2 Laban Movement Analysis (LMA)

LMA provides a model for observation, description and notational system for human movements. Implementation of the same in a computer has been done through Bayesian approach [11] and also by 3DTI in which choreographic process was enhanced by Nahrstedt et al. [12] using a 3D tele-immersive (3DTI) room surrounded by on multiple 3D digital camera and displays from a remotely placed dancer.

2.2 Grammar

Dance can be represented by capturing underlying rules of all possible movements. A grammar of all possible movements can be formulated and it shall help in further representation and processing. Some of the approaches use grammar to store body movements or all possible dance rules. Following subsections describe two such grammar related approaches.

2.2.1 Dance Grammar

Dance grammar is used for representation of dance (as shown in Figure 1) as well as for dance interpretation (as shown in Figure 3).

A grammar for gestures captured on a multi-touch device is derived and used by Puig et al. [13] to identify the different choreographic moves which shall help in recalling all corresponding clips from the movie. Bradford et al. [14] developed a tool CorX for exploration of dance grammar which is a set of executable if-then rules. Bradley et al. [15]

have given a corpus based grammar of joint movements. By using simple machine learning and search techniques authors have claimed to automate the process of animation for a prescribed body posture. Bull

[16] produced a formalised grammar of Aerobics Dance Exercise. This formed the Aerobics Corpus which was collected from a National Survey. Using Computational Linguistic techniques, he extracted a formalised grammar for Aerobic Dance choreographic moves.

2.2.2 Choreographic Language Agent (CLA)

Choreographic Language Agent (CLA) [17] helped to bridge the gap between the notations, sketches, diagrams and text done by the choreographer on a notebook and his thinking process. Thus an unique method was used to augment the thinking process of the choreographer.

3 Dance Capturing

Various techniques have been used for capturing dance movements of different styles. The most common being motion capture techniques and use of sensors. Dance motion can be captured using multi view vision camera or monocular vision camera. The obvious difference between usages of both is the additional cost factor and also the various dimensions of the object.

Capturing and modeling of all the dance movements correctly results in efficient processing too. Several attempts have been made by researchers to capture dance mainly with the help of sensors and motion capture cameras (Figure 2). The details of techniques used and research work thereof is presented in following subsections.

Figure 2: Dance Capturing

3.1 Sensors

A device which receives and responds to a signal on a certain preset event (say ‘when touched’) is a sensor. Several experiments have been carried out with the help of dance experts who wear different sensors on their body. The sensors are monitored to obtain dance movement data.

The touch interactive robot trained by Curtis et al. [18] has touch sensors residing on its tail, legs, back and head along with contact switches on the bottom of each foot which was taught motions via demonstration and reinforcement. Using 41 markers on dancers Qian et al. [19] have developed a real time gesture driven interactive system. This marker based motion capture systems were used to provide real-time marker positions in global space. Paradiso et al. [20]

have used a sensor system along with a small microprocessor and wireless transmitter in a pair of dancing shoes to capture the various degrees of freedom for the feet which can be made applicable for a series of computer-augmented dance performance. Aylward et al.

[21] have used a compact , wearable sensor system which enabled real time collective activity tracking for interactive dance. The dancer wore a wireless sensor at the wrists and ankles. Takasi et al. [22] used a Motion Capture system called as Eva Hires which is an optical motion capture system introduced in the Hiroshima city university for synthesizing dance data automatically for a user given music, even if the user has no knowledge of music at all. A multimedia system [23] uses a novel motion classification scheme. This is a wearable computer which aims to achieve a 3-D dance style classification for Butoh dance. This is a contemporary dance improvising method originating in Japan.

3.2 Motion capture

Motion capture is defined as “The creation of a 3D representation of a live performance”, by Alberto Menache in the book Understanding Motion Capture for Computer Animation and Video Games. Thus Motion Capture technology shall help in detailed analysis of the captured dance movement and use that data to develop ways to analyze the movement in detail.

3.2.1 Monocular Vision

Chen et al. [24] have used a single camera without any markers for Motion Capture to obtain 3D motion parameters of a human figure. This tracking of human figure on video has used image silhouettes. Paul et al. [25] have used a single camera to metamorphose the user into a virtual person who can be an off-site coach using low band-width joint motion data to permit real time animation. This metamorphosis involved altering the appearance of the person in this case a kathakali dancer since this dance style has elaborate costume and make-up which is very time consuming. The user did not have to wear any hardware device and the paper aimed at making gesture tracking simpler, cheaper and user-friendly. Curtis et al. [18] have trained a robot named Pleo from Innvo Corporation for treating autistic children with dance therapy. In this, a coloured camera is used in the nose of the friendly dinosaur looking robot, Pleo. Using a single camera for motion capture of BharataNatyam [26], a semiautomatic method was developed to track the body parts using skin colour detection. The authors restricted their model up to upper body part only.

3.2.2 Multi view Vision

Lapointe et al. [27]

have used genetic algorithm for real time generation of human computer choreography through choreogenetics algorithm. Using motion capture with 8 cameras and 8 PCs, Nakazawa et al.

[28] have used motion analysis method for recognising the structure of human dance motion and motion primitives for a Japanese folk dance “Soran Bushi”. Concatenating these primitives they have generated new dance motions using inverse kinematics and dynamic balancing techniques.

Brand et al. [29] have generated stylistic motion sequences from motion patterns through motion capture sequences. Data was captured by physical markers placed on human actors, over short interval of time, in motion capture studios. Qian et al. [19] have tackled motion capture’s marker occlusion problem by developing a real-time marker cleaning algorithm. Using 8 camera VICON systems for training, the multimodal feedback engine produced visual and audio feedback to the performer.

4 Dance Semantics

After representing and capturing dance, computational power can be used to process it. The model used to represent the dance, must enable user to interpret the processed dance steps. Thus processing the stored dance steps shall help in interpreting the meaning of the particular style of dance. Several techniques have been applied to understand the semantics of dance data from annotation, ontology, dance grammar or dance verbs, graph to vector space. These techniques used to interpret dance semantics have helped in the process of automating choreography. Figure

3 shows techniques under dance semantics and the details of each technique follows in subsequent subsections.

Figure 3: Dance Semantics

4.1 Annotation

Puig et al. [13] have experimented with Thierry de Mey’s Project for gestural annotation of dance video recordings. The grammar used by Mey to identify the different choreographic moves within the show and the annotation was processed through IRI’s software. To each identified gesture of the dancers, corresponding moves of the observer’s two hands were recorded through a multi-touch sensitive surface. Cabral et al. [30] have designed a video annotator for tablet PC using touch input interfaces. This is used for contemporary dance as a creative tool by choreographer to improve his work during rehearsal, live performances for later review or sharing notes with performers which can in future be also used for general web-based archive. Mallik et al. [31] have automatically annotated new instances of digital heritage constructed ontology for Indian Classical Dance BharataNatyam and Odissi to train multimedia data. E-dance project [32] showed how grid-based hypermedia and semantic annotations were used for capturing and rendering the choreographic practices. For content based multimedia access, concept recognition using ontology is used by Malik et al. [33]. The automatically annotated new instances enabled creation of semantic navigation environment in a cultural heritage repository. Video annotation by Malik et al. [34]

using the power of MOWL has provided an effective video browsing interface to the user through a Bayesian Network for Indian Classical Dance such as BharataNatyam, Odissi, Kutchipudi and Kathak including music performances like Hindustani and Carnatic music. 200 videos of about 10 to 15 minutes duration were used of all these Indian Classical Dances for experimentation.

4.2 Ontology

Mallik et al. [33], [31] have constructed ontology for Indian Classical Dance BharataNatyam and Odissi to train multimedia data and automatically annotate new instances of digital heritage. The domain knowledge has been encoded in ontology and has provided methods to co-relate this to the audio-visual recordings and other digital artifacts. An ontological framework for Indian Classical dance by Malik et al. [34] offered a robust ground for several multimedia search, retrieval and browsing applications. The system was self enhancing where ontology was refined from annotated data and data annotation was improved based on fresh, refined knowledge from the ontology.

4.3 Dance Grammar (Verbs)

A gestural annotation of dance video recordings to codify Mey’s [13] own movies and musical pieces in which he has elaborated a grammar of gestures. A multi touch sensitive surface records the observers’ two hands for each identified gesture which can help identifying all similar clips in the movie. Hsieh et al. [35] have used Newton’s law for presenting a set of dynamic models according to some dance verbs for contemporary dance. This paper was done with an aim to assist in computer-aided choreography and overcome the difficulty of dynamic based animation. Bradford et al. [14]

has described a program that uses Artificial Intelligence for dance. An if-then rule driver approach was used which described motion to create a sequence of dance phrases and this was used to generate choreography for multiple dances. A complex set of rules are thus created, executed and evaluated for patterns of dance.

4.4 Vector space

Jadhav et al. [36] have used vector space for modeling BharataNatyam. All major limbs of the body; head, hands (both right and left), waist and legs (both right and left) are coded as per the orientations, positions and strict norms of the Indian Classical dance, BharataNatyam. The major limbs of the body have Sanskrit names as per the ancient dance scripture Natyasastra and they have been modeled as per these names and the X, Y and Z axes respectively.

4.5 Graph

A graph based algorithm was proposed for reconstructing 3D model for BharataNatyam dance from tracked data. The authors Mamania et al. [26] proposed to visualize a pose as a node and a transition between two poses as an edge between corresponding nodes.Sugathan et al. [37] has extracted features from an attribute relation graph for the upper body poses of basic Bharatanatyam steps which can be useful for classification and annotation of dance poses.

5 Dance generation

Dance generation using computer system has been attempted by several researchers and some have been successful in fully or partially automating the process. We enlist a few methods that automated dance choreography by generating dance steps or poses through 3D, using robot motion to generate images. We have identified three major aspects of dance generation namely, animated dance steps, computer aided choreography and Image generation as shown in Figure 4. The details of each aspect are elaborated in three subsections.

Figure 4: Dance Generation

5.1 Animated dance steps

Nagata et al. [38] obtained Latin Dance movements using Motion Capture and attempted to extract the difference in the character of movement of experienced people (Latin people) as well as inexperienced (Japanese people) for Latin dance. Thus after the extraction of natural dance movements, animation was carried out to make use of the outcome. Using advanced 2D research techniques, they have confirmed a phase difference in the movement of shoulders and hips considered to be a characteristic movement of experienced Latin dancers. They claim that Japanese people are unfamiliar to a motion called ”isolation”. Wilke et al. [9] have developed the LabanDancer to translate Labanotation dance scores into 3-D human figure animation for a wide variety of different movements. The main reason being fewer dancers can read notation and even fewer are capable of producing scores. This can be a teaching tool for dance choreographers and students. Animation was done using Life Forms software by Sukel et al. [39] and all the participants performed three formal Ballet movements in same order and they filled out a demographic sheet The authors felt that computer animation provided a better joint segmentation thus improving learning as compared to videotapes. Mazumdar et al. [40] have generated a library of body movements based on rules of BharataNatyam and human body constraints which are further used to generate the dance steps for pure dance movements.

5.2 Computer aided choreography(CAG)

CAG is further sub categorized into fully automated and semi automated. CAG as discussed in following subsections.

5.2.1 Fully automated

Hagendoorn has transformed a dance studio [41] into a laboratory for studying complex systems for dance choreography. He has tried to find different equivalence relations and classes as they apply to dance so that fascinating patterns emerge within a group of dancers. He has formulated a set of what he called as nature inspired rules that determined the interaction among a group of dancers. The system results in patterns that indicate how dancers should move on stage. Although the focus of the work is limited to movement of dancers on stage, the system generates fully automated choreography. Nakazawa et al. [42] have used genetic algorithms for a fully automated system of waltz choreography. They used mutation and crossover for exploring possible solutions to obtain a global optimum. The system generated satisfactory results by using majority of the stage, keeping partners facing each other and dancers on stage. The authors claimed that the system resulted within 10 % of the optimal choreography. Using the Choreogenetics algorithm [27], choreographic variants were obtained for five basic movements. These were selected based on aesthetic criteria. Lapointe et al. [43] have shown that the best mutants closely match with the virtual dancer and thus the duets generated by the algorithm are not entirely random. Takahashi et al. [22] proposed a dance synthesis system that utilizes the motion capture data with Computer Graphics software Maya according to the impressions of music. Yu et al. [44] tried to demonstrate the workability and usefulness of computer generated choreography by using swarm toolkit and Life forms software with multi-agent system. The swarm toolkit was used to generate a sequence of dance steps which was later animated and expert evaluation is sought. Jadhav et al. [45],[46], [47],[48] have created an Indian Classical Dance choreography generator termed as ArttoSMart which displays several possible sequences for pure dance movements in BharataNatyam, given the number of beat and a starting pose as an input. Thus the user can choose the best possible sequence from the available options.

5.2.2 Semi-automated

Stuart et al. [15]

have developed and used corpora of human movements comprising of ten Balanchine ballets to select a movement sequence that would naturally occur between a given pair of body postures. They have applied techniques from graph theory, Artificial Intelligence and statistics to the above corpus of movement sequence. Interpolation methods are described in this paper to automatically construct interpolation sequence that suggest moves from one specified body posture to another in a physically and stylistically coherent fashion. Curtis et al.

[18] has designed a system that could be trained to learn dance movements through visual and haptic cues. With human assistance the robot could learn and perform dance steps. Bradford et al. [14]

utilized rule driver embodying a heuristic algorithm for choreography of dance. They have designed a system CorX which may prove valuable to choreographers as an aid to the creative process but many of the details of interpretation are left to the human choreographers.

5.2.3 Image generation

Pattanaik [49] has tried animating a few BharataNatyam karanas using stick figure model initially and finally a stylised volumetric model was used which could convey the position of the body efficiently and correctly. Jadhav et. al [50] have automated the process of 2D Stick Figure generation from the 30 attribute human body model [36].

6 Dance Processing Approaches

Capturing and modeling of all the dance poses effectively ensures efficient processing to obtain automated dance movements. Several attempts have been made by researchers in various ways to process these captured and modeled data, refer Figure 5

. These approaches and corresponding research work are explained in later subsections. The approaches discussed in this paper are as follows. Evolutionary Programming using Genetic, Flock and Ant optimization Algorithms; Classification using Neural networks and Support Vector Machines; Image Processing for gesture recognition; Graph based algorithms; Corpus Based and Multi agent system.

Figure 5: Dance Processing Approaches

6.1 Evolutionary Approach

This approach works on the powerful principle of evolution i.e. survival of the fittest [51] which models natural phenomena like genetic inheritance and Darwinian strife for survival using heuristic search.

6.1.1 Genetic Algorithms

A Genetic Algorithm based paper [27] that generates aesthetic choreography through mutations and selection and a new algorithm is applied to simulate the evolution of a sequence of dance movements. Using GA to create human computer choreography for real-time performance environments, Lapointe et al. [43] have created human computer duet by using motion capture technique on actual performers and coded the same with LIFE animation software to create a virtual vocabulary of four movements: run, jump, turn and fall. Nakazawa et al. [42] have used genetic algorithms for a fully automated system of waltz choreography. The fitness function was designed with respect to following equally weighted factors like the couple’s position, use of stage, step sequence, closeness to ideal steps, measurement of stage and so on. Jadhav et al. [45], [46], [47], [48] have used Genetic Algorithm to find static dance poses for single beat and Multi Beat with the use of a fitness function which tries to converge with results neither too close nor far from the ideal pure dance movements called as ”adavus” for the Indian Classical Dance, BharataNatyam.

6.1.2 Flock

Hagendoorn [31] has used flock technique for generation of enticing patterns of dance. He says that within a flock only nearest neighbors are visible and hence it is used for self- reinforcing of dance patterns. Rules governing the behavioral pattern of agents are listed out and most of the rules are inspired by the flock of birds or swarm behavior.

6.2 Classification

Classification is a technique where the user knows ahead how classes are defined. It is necessary that each record in the data-set, used to build the classifier. already have a value for the attribute used to define classes. Dance Processing can also be done using various classification techniques like Neural Networks and Support Vector Machines.

6.2.1 Neural Networks

Dubbin et al. [52]

presented a program that takes advantage of interactive evolutionary computation and Artificial Neural Networks to train virtual humans to learn to dance. The dancers were controlled by ANN. They have efficiently solved the problem to parse sound in a way that ANN could interpret it.

6.2.2 Support Vector Machine(SVM)

Using k-means clustering algorithm Mallik et al.


have trained an SVM classifier for classifying the media patterns. A training set of video segments were labeled by domain experts which helped in creating the multimedia enriched ontology and use of machine Learning algorithms re validated the same with the use of low level media features and SVM classification. This was further used to interpret the media features extracted from a larger collection of videos to classify them into semantic groups. Sharma

[53] has used an action classifier for the basic BharataNatyam dance moves called as adavus using SVM.

6.3 Graph Based algorithms

Bradley et al. [15]

have designed interpolation algorithm for a Ballet dancer’s body postures. These movements remain consistent from one prescribed form to another. The use of transition graphs for capturing transition probabilities for every body-joint and generating a small corpus for ballet sequences and finally interpolation sequences and applying A* search has resulted in learning the grammar of dance. The graph-theoretic methods learn the grammar of joint movements in a given corpus.

Feature extraction was done by Hariharan et al. [54] by generating a feature vector for distinguishing between different gestures of a BharataNatyam dancer’s single hand gestures. The silhouette was extracted followed by the generation of the corresponding skeleton and the evaluation of the gradients at its end points. Morphological operators are used to obtain a skeleton which corresponds to graph called as connectivity graph. Several such connectivity graphs for different hand gestures are shown. Sugathan et al.[37] has proposed a graph based model for identifying and classifying the 2D poses of a BharataNatyam dancer’s upper body.

6.4 Image Processing through Gesture recognition

Quin et al. [19] have proposed a gesture recognition engine which provides real time recognition of the performer’s gesture, based on the 3D marker co-ordinates. Hariharan et al. [54] developed a prototype for the recognition of the 28 single hand gestures of BharataNatyam called as Asamyukta Hastas in a 2D space using image processing techniques whereas Saha et al. [55] have used boundary of the hand gesture and texture based segmentation to sort out the flaws for recognition of the same single hand gestures. Emotions of the Indian Classical Dancer have been captured through the Kinect Camera by Saha et al. [56] for creating a gesture recognition algorithm and in [57] for automatic BharataNatyam hand gesture recognition. Sharma [53] has used the Kinect camera in his M.Tech. thesis, to capture and recognize the basic BharataNatyam adavus.

6.5 Corpus Based

Bradley et al. [15] have developed a corpus- based interpolation algorithm for movement sequences to achieve a ballet dancer’s move from one body posture to another for computer animation. Bull [16] has designed a corpus of Aerobic Dance Exercise routines for analysis using linguistics techniques. He has named it ACCOLADE: A Computerized Corpus of Legal Aerobic Dance Exercises. Later on he used the real time animation of dancers with the NUDES system developed by University of Sydney. This was successfully applied to his Aerobics project.

6.6 Multi agent system

The prototype system proposed by Hagendoorn [41] is based on the concept of modern multiagent system. This term (multiagent system) is not explicitly used by the author but each dancer is assumed to be an agent and determines his/her behavior by sensing the state of other agents (individual or as a group).

7 Dance Visualization

Live dance performances can use different forms of multimedia. These forms can be preplanned animation sequences or the use of digital video with appropriate sound at the background along with live dancers. These performers can interact with this pre programmed display. A more technically challenging system would be to sense the live dancers and modify the sound or images accordingly or having two distant locations for rehearsal and performance. Thus various possibilities are available and a challenging problem of computer graphics and interactive technologies for live performers can be observed here.

Stage visualization has been effectively done by Calvert et al. for enhancing live performances [58] and edited by Potel. The use of computer graphics has been conceptualized to visualize choreography, composing, editing and animating dance notation for enhancing live performances. This tool helped the choreographer to try out ideas before meeting with the live dancers. The software has human male and female figures for ballet and modern dance and thus a choreographer can easily plan even in scarce dance studios. A virtual dance studio has been created by National Arts Center, Canada through their website [59] where various movements of a ballet dancer are placed and options available for the viewer to choose the start, middle and end sequence. Finally these entire movements are co-ordinated in a sequence and played for visualization purpose. Dubbin et al. [52] presented a model called Dance Evolution allowing the user to train virtual humans to dance to MIDI songs or raw audio. This was implemented using Panda 3D simulator.

8 Dance Robotics (Humanoid)

Humanoid robots are used in several research areas from domestic help to space and dance is one such area where it has been successfully implemented. Shinozaki et al. [60] have proposed a humanoid dance robot system for a hip-hop dance sequence. Based on a professional dancer’s basic movements, sixty dance units were extracted and these short dance motions were concatenated for a robot dance system. These resultant concatenated units created huge amounts of dance variations for the hip-hop genre. Gruberg et al. [61] have experimented with the motions of a robot that are co-coordinated automatically to the music beat. They have used a real time music signal thus enabling humanoid robot to dance autonomously. Nakazawa et al. [28] have proposed a dance motion imitation for humanoid robots through visual observation. Using a stochastic controller, Angulo et al. [62] have tried to generate dance movements adequate to the music rhythm. They have tried to recreate a human-robot interaction system with the Aibo robot.

9 Applications of Automated Dance Systems

There are several advantages of computerized modeling of dance. The biggest commercial industry is of entertainment and gaming followed by e-learning, distance learning, computerized dance tutor. Additionally dance systems applications can be found in robots used for medical therapy for differently-abled children and also for the sake of heritage preservation of various classical dance forms and dying art. All these application areas shown in Figure 6 and explained thereafter.

Figure 6: Dance System Applications

9.1 Entertainment

Nirvana Technologies developed robot dance system has been used by Shinozaki et al. [60] for dancing various moves which are short concatenated dance motions performed by the humanoid robot. The hip-hop style was chosen over ballet since the later does not have specific rules for the details of whole body motions.

9.2 E-learning / distance learning

Paul et al. [25] showed how a renowned Kathakali dancer from a remote site may provide feedback to a student which is an excellent resource for e-learning. Both the master and the disciple can zoom on a particular performance to view the scene from a vantage point or repeat action in slower speed. Using a 3D tele-immersive (3DTI) room surrounded by multiple 3D digital camera and displays from a remotely placed dancer [12] visual stimulations have helped in distance learning. To understand and extend choreographic knowledge, e-dance project [32] was used where performers and spectators were co-present in physical spaces and simultaneously shared multiple, virtual locations. Computer is used as a teacher by Hariharan et al. [54] in an e-learning environment to rectify the twenty eight single hand gestures of BharataNatyam (Asamyukta Hastas) performed by students whereas Saha et al. [57] have been able to improve on time, efficiency and accuracy for the same single hand gestures through the use of fuzzy L Membership function and in [55] through polygon representation. A mobile based applet was used by Mazumdar et al. [40] to teach BharataNatyam adavus through a puzzle based game. Using the kinect sensor, Saha et al. [56] have extracted positive or negative emotions from the gestures made by the Indian Classical Dancer which can be thus used for learning and evaluating the performance.

9.3 Heritage Preservation

Digital Multimedia technology helps in preservation of heritage [31] and also enhances its accessibility over a prolonged period of time. This paper has correlated the digital resources with the traditional knowledge of Indian Classical Dance BharataNatyam and Odissi .

A Russian BharataNatyam dancer Annemette Karpen [7] has used Labanotation to notate BharataNatyam dance so as to record and preserve the heritage of Indian Classical dance.

9.4 Medical Therapy

Dance Movement therapy has been used effectively for treatment of wide range of physical and mental disorders. Curtis et al. [18] has designed Pleo which is a low-cost, off the shelf, robotic platform designed specially to be a trained robotic dance therapy assistant capable of engaging autistic children. The desired dance motions and music are provided to the robot by the dance therapist who in turn introduces the same to the patient and this can be provided for home use to accelerate the therapy process. Thus a low-cost dancing robot has been trained for medical therapy.

9.5 Tutor

Sukel et al. [39] developed a computer based dance tutor which is a virtual aid to teach formal ballet so that it may provide individualized attention that dance classes lack due to higher student-teacher ratio.

10 Conclusion

Creativity is considered to be a gift and no two persons can be same in this process. A field of art like Dance is considered to be entirely a creative process although the basics are very clearly specified and taught in case of Classical Dance forms. Each choreographer is known for their particular style and creative form. Using a machine to aid in this creative process has been attempted by many well-known creative artists and dance is also a domain known to accept and start experimenting with this. We have reviewed at least hundred of such research papers and articles and categorized them accordingly.

We have identified six such major categories for classification and discussed how the research work has been carried out in these areas. The Dance forms range from Indian Classical like BharataNatyam, Kutchipudi, etc to Western Classical like Ballet. Also some Japanese folk dance form has been used like Soran Bushi and contemporary style like Butoh. Our aims for writing this review of dance papers are to make it easier for the researcher who is interested in automation of the choreographic process like us.


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