We show that, from the output of a simple 3D human pose tracker one can infer physical attributes (e.g., gender and weight) and aspects of mental state (e.g., happiness or sadness). This task is useful for man-machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods (vs. conventional Euclidean joint error metrics). 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; We show that inference is possible even 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 the mocap helps to regularize such models.While video-based 3D pose estimates are noisy, they do support the inference of human attributes.