Style-based Motion Analysis for Dance Composition


Style-based Motion Analysis for Dance Composition

Andreas Aristidou, Efstathios Stavrakis, Margarita Papaefthimiou, George Papagiannakis, Yiorgos Chrysanthou

The Visual Computer, 2017.

This work presents a motion analysis and synthesis framework, based on Laban Movement Analysis, that respects stylistic variations and thus is suitable for dance motion synthesis.

[DOI] [paper] [bibtex]

The dance motion capture data used can be downloaded from the Dance Motion Capture Database website.


Abstract


Synthesizing human motions from existing motion capture data is the approach of choice in most applications requiring high- quality visual results. Usually to synthesize motion, short motion segments are concatenated into longer sequences by finding transitions at points where character poses are similar. If similarity is only a measure of posture correlation, without consideration for the stylistic variations of movement, the resulting motion might have unnatural discontinuities. Particularly prone to this problem are highly stylized motions, such as dance performances. This work presents a motion analysis framework, based on Laban Movement Analysis, that also accounts for stylistic variations of the movement. Implemented in the context of MotionGraphs, it is used to eliminate potentially problematic transitions and synthesize style-coherent animation, with out requiring prior labeling of the data. The effectiveness of our method is demonstrated by synthesizing contemporary dance performances that include a variety of different emotional states. The algorithm is able to compose highly stylized motions that are reminiscent to dancing scenarios using only plausible movements from existing clips.

The main contributions of this work include:

  • A novel style-coherent motion analysis algorithm based on Laban Movement Analysis principles.
  • A framework for synthesizing style-coherent animation based on Laban Movement Analysis, without requiring prior labeling of the data.
  • A style-preserving motion synthesis technique that can be readily used with the popular Motion Graphs algorithm.




© 2017 Andreas Aristidou