The Taub Faculty of Computer Science Events and Talks

This talk concerns the estimation of human attributes from 3D human pose and motion. We consider both physical attributes (eg, gender and weight) and aspects of mental state (eg, mood). This task is useful for man-machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods. Based on an extensive corpus of motion capture data, with physical and perceptual ground truth, we analyze the inference of subtle biologically-inspired attributes from cyclic gait data. It is shown that inference is also possible with partial observations of the body, and with motions as short as a single gait cycle. Learning models from small amounts of noisy video pose data is, however, prone to over-fitting. To mitigate this we formulate learning in terms of domain adaptation, for which mocap data is uses to regularize models for inference from video-based data.