Massimiliano Balduzzi: Research in Physical Training for Performers

This essay begins the process of contextualizing and analyzing Massimiliano Balduzzi’s solo physical training practice by introducing six newly created video documents. It locates Balduzzi’s work in a wider historical and artistic context – touching upon the work of Konstantin Stanislavski, Jerzy Grotowski, and Eugenio Barba, as well as acrobatics, martial arts, and Balinese dance – while arguing that the documented physical training constitutes an original research contribution to the field of embodied technique. The essay has three main purposes: First, to give verbal articulation to some important aspects of Balduzzi’s practice, as he begins to teach more widely in New York City and beyond. Second, to test and develop a theoretical framework that conceives of embodied technique as a field of knowledge in which rigorously framed research can and does give rise to new knowledge in the form of new technique. Third, to explore the epistemological status of multimedia documentation through a focused case study. Each of these goals has the potential to expand and clarify current discussions of actor and performer training, movement analysis and documentation, and practice-as-research

RGBD Datasets: Past, Present and Future

Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released. These have propelled advances in areas from reconstruction to gesture recognition. In this paper we explore the field, reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification. By extracting relevant information in each category we help researchers to find appropriate data for their needs, and we consider which datasets have succeeded in driving computer vision forward and why. Finally, we examine the future of RGBD datasets. We identify key areas which are currently underexplored, and suggest that future directions may include synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201