Virtual Reality and Choreographic Practice:The Potential for New Creative Methods

Virtual reality (VR) is becoming an increasingly intriguing space for dancers and choreographers. Choreographers may find new possibility emerging in using virtual reality to create movement and the WhoLoDancE: Whole-Body Interaction Learning for Dance Education project is developing tools to assist in this process. The interdisciplinary team which includes dancers, choreographers, educators, artists, coders, technologists and system architects have collaborated in engaging, discussing, analysing, testing and working with end-users to help with thinking about the issues that emerge in the creation of these tools. The paper sets out to explore the creative potential of VR in the context of WhoLoDancE and how this may offer new insights for the choreographer and dancer. We pay attention to the virtual environment, the virtual performance and the virtual dancer as some of the key components for equipping the choreographer to use in the creating process and to inform the dancing body. The cyclical process of live body to virtual, back to the dancing body as a choreographic device is an innovative way to approach practice. This approach may lead to new insights and innovations in choreographic methods that may extend beyond the project and ultimately take dance performance in a new direction

A study on smoothing for particle-filtered 3D human body tracking

Stochastic models have become the dominant means of approaching the problem of articulated 3D human body tracking, where approximate inference is employed to tractably estimate the high-dimensional (~30D) posture space. Of these approximate inference techniques, particle filtering is the most commonly used approach. However filtering only takes into account past observations - almost no body tracking research employs smoothing to improve the filtered inference estimate, despite the fact that smoothing considers both past and future evidence and so should be more accurate. In an effort to objectively determine the worth of existing smoothing algorithms when applied to human body tracking, this paper investigates three approximate smoothed-inference techniques: particle-filtered backwards smoothing, variational approximation and Gibbs sampling. Results are quantitatively evaluated on both the HUMANEVA dataset as well as a scene containing occluding clutter. Surprisingly, it is found that existing smoothing techniques are unable to provide much improvement on the filtered estimate, and possible reasons as to why are explored and discussed