Just Dance Moves for Creativity
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The document describes research into developing an automated tool to assist choreographers in their creative process. A survey found that some choreographers are interested in such a tool. A proof-of-concept mobile app was created with different dance styles and rule-based strategies for generating variations. An evaluation found that variations based on an ontology were better than random variations. Presentation through 3D animation was preferred over text, 2D animation or audio. Future work includes developing better dance movement representations and reasoning capabilities.
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Just Dance Moves for Creativity
- 2. Just Dance 4
- 4. Variation from (Victoria Zhou) 1. Step forward towards corner 2, into croisé derrière à terre, arms demi- seconde 2. Plié in 5th change direction to corner 1 3. Relevé derrière, arms in 4th en avant palms down 4. Passé and change direction to corner 2 place foot on pointe in 5th, arms move to a low kissing gesture 5. Retiré and back to 5th on pointe with the front foot, back foot and front foot arms move to demi-seconde 6. Repeat to the right, and repeat all. 7. Posé coupé effacé towards corner 1, arms in 4th en avant 8. Posé coupé arms in 4th palms down 9. Step and posé retiré croisé, place back foot in 5th on pointe, port de bras with the left arm from 5th en haut to 5th en avant, end with the arms forward in a low line, cross the wrists.
- 7. What representation for dance? for humans and machines Sci-FiDancingGIFbyColinRaff
- 8. While for music we have good (machine readable) representations, we lack these for dance.
- 9. Why do we need knowledge representation for dance? Three reasons
- 13. Towards a choreography assistant tool Sensing Representation + Reasoning Presentation generation • Motion detection • Floor sensors • Move recognition • Dance movement representation • Dance choreography representation • Use of background knowledge • Pattern detection • Choreography generation • Visual presentation • 3-D animation • Auditory presentation Sensing data Choreography variation Presentation Choreography
- 14. Existing representations and tools
- 15. Labanotation
- 16. LabanXML and Laban Editor LED Labanotation editor http://donhe.topcities.com/pubs/led.htmlNakamura & Hachimura (2006)
- 17. Benesh
- 18. Dance Forms
- 19. Cecchetti system 7 elementary movements: [plie (bend), etandre (stretch), releve (rise), sauter (jump), glisse (glide), tourne (turn), elancer (dart)] Positions: 1st, 2nd, 3rd,.. (left right croise) Facing position (1…8) Position in space Direction of movement (de cote, dessous, dessus, en avant, en arriere, devant, derriere) Combinations (100+) pas-de-chat, pas-de- bourre, piroutte Ballet languages/systems Based on interview with Marije Koning
- 20. XML Dance Grammar Balakrishnan Ramadoss and Kannan Rajkumar. Modeling the Dance Video Semantics using Regular Tree Automata Fundamenta Informaticae 86 (2008) 175–189 175 IOS Press
- 21. Interactive Dance Choreography Assistance Victor de Boer Josien Jansen Ana-Liza Tjon-A-Pauw Frank Nack
- 22. Sensing Representation + Reasoning Presentation generation • Motion detection • Floor sensors • Move recognition • Dance movement representation • Dance choreography representation • Use of background knowledge • Pattern detection • Choreography generation • Visual presentation • 3-D animation • Auditory presentation Sensing data Choreography variation Presentation Choreography
- 23. To what extent can choreographers be supported by semi-automatic dance analysis and the generation of new creative elements in choreographies?
- 24. Method Questionnaire: How do choreographers work (with technologies) Tool: Proof of concept digital choreo assistant Evaluation: Test application to and different strategies
- 25. Questionnaire 54 Dutch choreographers Online questionnaire
- 26. Personal choreography archiving 0 2 4 6 8 10 12 14 16 18 written dance notation digital dance notation videotaping other memory, without problems memory, forget things
- 28. Notations Laban & Benesh 0 5 10 15 20 25 30 Never heard of it Cannot work with it Other Know Laban Can write both
- 29. Interest in support in the creative process Originality, Creativity and Emotion are most important aspects One very negative sub-group > Afraid to lose humanity One positive towards creative assistance Two sub-groups:
- 30. Tool Requirements based on MoSCoW method • A dancer must be able to add their dance style to the tool • A dancer must be able to add their existing choreography to the tool • The tool must be able to give new suggestions for variations of the choreography • The suggestions must be based on different strategies • The dancer must be able to see the whole choreography at any moment in time (written) • The communication of the tool are written dance terms • The tool must be “easy to use”, which means getting suggestions may take no longer than 2 minutes • The tool does have simplified body movements (legs, feet, arms, hands and head)
- 31. Proof-of-concept mobile app 3 different dance styles Ballet (including 78 steps) Modern dance (including 57 steps) Street dance (including 31 steps) Dancepiration – a tool for choreography assistance
- 32. 4 rule-based strategies for creating variations on existing choreographies 1. Random step replaced by random other step 2. Random step replaced by ontology-based other step 3. Random steps replaced by multiple strategies 4. Specific step replaced by ontology-based steps
- 33. Ontology-based variation for the 3 dance styles . El Raheb, et al. BalOnSe: Ballet Ontology for Annotating and Searching Video performances. In Proceedings of the 3rd International Symposium on Movement and Computing (p. 5). ACM, 2016
- 35. 6 choreography students Random-based versus Ontology-based Each dance style is tested 3 times with both strategies per person Rate original choreography and each variation (10pt scale) Rate on 5pt Likert scale: Correctness, Creativity, Helpfulness, Meaningfulness
- 36. Results Respondents are positive about the tool …prefer to choose a specific step to change themselves … consider creativity in this tool very high (avg 4.2/5) Correctness is important to improve, it influences other factors the most
- 37. Ontology-based variant outperforms random variations Score OriginalRandomOntology-BasedDifference Sig Average grade 6.17 5.50 6.35 +0.85 ** Correctness 2.89 3.37 +0.48 * Creativity 3.19 3.37 +0.18 Helpfulness 2.59 3.00 +0.41 Meaningfulness 2.70 2.96 +0.26
- 38. Style matters *=statisticallysignificantatα=0.10(t-test/anova) **=statisticallysignificantatα=0.05(t-test/anova) Element Style Random Ontology-Based Difference Correctness Ballet 2.89 2.56 -0.33 Streetdance 2.78 3.56 +0.78 * Modern 3.00 4.00 +1.00 ** Creativity Ballet 3.44 3.56 +0.12 Streetdance 2.78 3.11 +0.33 Modern 3.11 3.44 +0.33 Helpfulness Ballet 2.67 2.67 0.00 Streetdance 2.44 2.89 +0.45 Modern 2.89 3.44 +0.55 Meaningfulness Ballet 2.89 2.78 -0.11 Streetdance 2.33 2.67 +0.34 Modern 2.89 3.44 +0.55
- 39. Sensing Representation + Reasoning Presentation generation • Motion detection • Floor sensors • Move recognition • Dance movement representation • Dance choreography representation • Use of background knowledge • Pattern detection • Choreography generation • Visual presentation • 3-D animation • Auditory presentation Sensing data Choreography variation Presentation Choreography
- 40. Which presentation methods are considered most effective for an interactive dance choreography assistant tool?
- 41. Experiment: Comparing 4 choreography presentation methods
- 44. 3: 3D animations DanceForms 2 (http://charactermotion.com/products/danceforms/
- 46. Experiment• 7 choreographers • 2 new(!) styles – Hip-hop and dancehall • Simple choreography + pre-generated variations • Large projection screen in practice space • 4 presentation variations (random)
- 48. Overall assessment Dancehall vs Hip-hop 0 1 2 3 4 5 6 7 8 9 10 Textual 2D animations 3D animations Auditory Score 0 1 2 3 4 5 6 7 8 9 10 Textual 2D animations 3D animations Auditory Score
- 49. 0 1 2 3 4 5 6 7 8 9 10 Overall assessment Stimulation of creativity Understandability (Un-)disruptiveness Textual 2D an. 3D an. Auditory 3D animations are the best
- 50. A significant sub-group of choreographers is interested in and enthusiastic about automatic choreography support Needs to be able to understand ‘dance language’ Knowledge representation matters Style matters Presentation styles matter -> 3D + dance language
- 51. Sensing Representation + Reasoning Presentation generation • Motion detection • Floor sensors • Move recognition • Dance movement representation • Dance choreography representation • Use of background knowledge • Pattern detection • Choreography generation • Visual presentation • 3-D animation • Auditory presentation Sensing data Choreography variation Presentation Choreography Next level: Representation and Reasoning Multi-tiered semantic model Low-level image features Atomic movements (Labanotation?) Compound movements (100+ movements) Emotional content, Socio-cultural layers etc. Machine Learning for classification and pattern detection Generative module (automatic choreographer)
- 52. Thank you v.de.boer@vu.nl @victordeboer http://victordeboer.com
- 54. Sensing • Motion capture – Marker-based – Marker-less • Joint rotations, limb positions etc. – unintuitive • Backup: Video annotation img: news.stanford.edu
Notas do Editor
- Sensing: The environment can be used in different stages of the choreography process. The choreographer can start with a semi-finished dance piece or only have a few dance phrases. The ambient environment detects the current choreography through computer vision, pressure sensors in floor and walls or other modalities. The existing choreography or parts of it are translated into a higher level dance representation language. Creative Reasoning: The current choreography is transferred to the creative reasoning engine in real time. Here, variations on the choreography are generated. This tool can produce new choreography parts varying spatial positioning, the number of performers, variations of dance phrases, repetitions of dance phrases and differences in expressions of movements. Background knowledge about similar or completely different choreographies as well as different dance styles can also be used to modify the choreography. This background knowledge is also stored in the higher level representation language. All operations on the current choreography can be determined by the user, but a more serendipitous use of the tool is also possible. Multiple variations of one choreography can be generated at the same time. Presentation: These newly generated choreographies are then presented to the choreographer. For this, 3-D video images of the new choreographies are projected on the walls of the environment. In this manner, the dancer/choreographer can absorb the variations while moving around the environment and he/she can choose to copy entire dance phrases or incorporate only parts of the suggestions. The ambient environment will detect the new movements in real time, creating a creative closed loop where choreographer and the environment work together to create a new dance piece. The tool can also be used offline. The tool then provides the choreographer with an in depth analysis of the current choreography through a dancer-friendly interface.
- Al these systems very academic, can be read by a few, mostly used for archival purposes
- Already more at normal level of dancer communication
- VU + UvA logo + fotootjes
- When looking at the results from dance style perspective, it seems that ballet is the worst performing dance style. The correctness of ballet is the lowest in comparison to the other dance styles. It is also the only dance style whereby the random variant performs better than the ontology-based variant.