Using music and motion analysis to construct 3D animations and visualisations

This paper presents a study into music analysis, motion analysis and the integration of music and motion to form creative natural human motion in a virtual environment. Motion capture data is extracted to generate a motion library, this places the digital motion model at a fixed posture. The first step in this process is to configure the motion path curve for the database and calculate the possibility that two motions were sequential through the use of a computational algorithm. Every motion is then analysed for the next possible smooth movement to connect to, and at the same time, an interpolation method is used to create the transitions between motions to enable the digital motion models to move fluently. Lastly, a searching algorithm sifts for possible successive motions from the motion path curve according to the music tempo. It was concluded that the higher ratio of rescaling a transition, the lower the degree of natural motio

Using music and motion analysis to construct 3D animations and visualizations

This paper presents a study into music analysis, motion analysis and the integration of music and motion to form creative natural human motion in a virtual environment. Motion capture data is extracted to generate a motion library, this places the digital motion model at a fixed posture. The first step in this process is to configure the motion path curve for the database and calculate the possibility that two motions were sequential through the use of a computational algorithm. Every motion is then analysed for the next possible smooth movement to connect to, and at the same time, an interpolation method is used to create the transitions between motions to enable the digital motion models to move fluently. Lastly, a searching algorithm sifts for possible successive motions from the motion path curve according to the music tempo. It was concluded that the higher ratio of rescaling a transition, the lower the degree of natural motion

Proof of Concept For the Use of Motion Capture Technology In Athletic Pedagogy

Visualization has long been an important method for conveying complex information. Where information transfer using written and spoken means might amount to 200-250 words per minute, visual media can often convey information at many times this rate. This makes visualization a potentially important tool for education. Athletic instruction, particularly, can involve communication about complex human movement that is not easily conveyed with written or spoken descriptions. Video based instruction can be problematic since video data can contain too much information, thereby making it more difficult for a student to absorb what is cognitively necessary. The lesson is to present the learner what is needed and not more. We present a novel use of motion capture animation as an educational tool for teaching athletic movements. The advantage of motion capture is its ability to accurately represent real human motion in a minimalist context which removes extraneous information normally found in video. Motion capture animation only displays motion information, not additional information regarding the motion context. Producing an “automated coach” would be too large and difficult a problem to solve within the scope of a Master's thesis but we can perform initial steps including producing a useful software tool which performs data analysis on two motion datasets. We believe such a tool would be beneficial to a human coach as an analysis tool and the work would provide some useful understanding of next important steps towards perhaps someday producing an automated coach

Entropy-based motion extraction for motion capture animation

In this paper we present a new segmentation solution for extracting motion patterns from motion capture data by searching for critical keyposes in the motion sequence. A rank is established for critical keyposes that identifies the significance of the directional change in motion data. The method is based on entropy metrics, specifically the mutual information measure. Displacement histograms between frames are evaluated and the mutual information metric is employed in order to calculate the inter-frame dependency. The most significant keypose identifies the largest directional change in the motion data. This will have the lowest mutual information level from all the candidate keyposes. Less significant keyposes are then listed with higher mutual information levels. The results show that the method has higher sensitivity in the directional change than methods based on the magnitude of the velocity alone. This method is intended to provide a summary of a motion clip by ranked keyposes, which is highly useful in motion browsing and motion retrieve database system

Compression de données d'animation acquises par capture de mouvements

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