Recent progress in computer-based visual recognition heavily relies on machine learning methods trained using large scale annotated datasets. While such data has made advances in model design and evaluation possible, it does not necessarily provide insights or constraints into those intermediate levels of computation, or deep structure, perceived as ultimately necessary in order to design reliable computer vision systems. This is noticeable in the accuracy of state of the art systems trained with such annotations, which still lag behind human performance in similar tasks. Nor does the existing data makes it immediately possible to exploit insights from a working system - the human eye - to derive potentially better features, models or algorithms. In this talk I will present a mix of perceptual and computational insights resulted from the analysis of large-scale human eye movement and 3d body motion capture datasets, collected in the context of visual recognition tasks (Human3.6M available at http://vision.imar.ro/human3.6m/, and Actions in the Eye available at http://vision.imar.ro/eyetracking/). I will show that attention models (fixation detectors, scan-paths estimators, weakly supervised object detector response functions and search strategies) can be learned from human eye movement data, and can produce state of the art results when used in end-to-end automatic visual recognition systems. I will also describe recent work in large-scale human pose estimation, showing the feasibility of pixel-level body part labeling from RGB, and towards promising 2D and 3D human pose estimation results in monocular images.In this context, I will discuss perceptual, perhaps surprising recent quantitative experiments, revealing that humans may not be significantly better than computers at perceiving 3D articulated poses from monocular images. Such findings may challenge established definitions of computer vision `tasks' and their expected levels of performance.