State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose to model the statistical relationships of 3D hand poses and corresponding depth images using two deep generative models with a shared latent space. By design, our architecture allows for learning from unlabeled image data in a semi-supervised manner. Assuming a one-to-one mapping between a pose and a depth map, any given point in the shared latent space can be projected into both a hand pose and a corresponding depth map. Regressing the hand pose can then be done by learning a discriminator to estimate the posterior of the latent pose given some depth maps. To improve generalization and to better exploit unlabeled depth maps, we jointly train a generator and a discriminator. At each iteration, the generator is updated with the back-propagated gradient from the discriminator to synthesize realistic depth maps of the articulated hand, while the discriminator benefits from an augmented training set of synthesized and unlabeled samples. The proposed discriminator network architecture is highly efficient and runs at 90ḞPS on the CPU with accuracies comparable or better than state-of-art on 3 publicly available benchmarks.

In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-theart methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features. In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition. Within the network structure, we design rotation mapping layers to transform the input Lie group features into desirable ones, which are aligned better in the temporal domain. To reduce the high feature dimensionality, the architecture is equipped with rotation pooling layers for the elements on the Lie group. Furthermore, we propose a logarithm mapping layer to map the resulting manifold data into a tangent space that facilitates the application of regular output layers for the final classification. Evaluations of the proposed network for standard 3D human action recognition datasets clearly demonstrate its superiority over existing shallow Lie group feature learning methods as well as most conventional deep learning methods.

We present a simple and effective method for 3D hand pose estimation from a single depth frame. As opposed to previous state-of-the-art methods based on holistic 3D regression, our method works on dense pixel-wise estimation. This is achieved by careful design choices in pose parameterization, which leverages both 2D and 3D properties of depth map. Specifically, we decompose the pose parameters into a set of per-pixel estimations, i.e., 2D heat maps, 3D heat maps and unit 3D directional vector fields. The 2D/3D joint heat maps and 3D joint offsets are estimated via multitask network cascades, which is trained end-to-end. The pixel-wise estimations can be directly translated into a vote casting scheme. A variant of mean shift is then used to aggregate local votes while enforcing consensus between the the estimated 3D pose and the pixel-wise 2D and 3D estimations by design. Our method is efficient and highly accurate. On MSRA and NYU hand dataset, our method outperforms all previous state-of-the-art approaches by a large margin. On the ICVL hand dataset, our method achieves similar accuracy compared to the nearly saturated result obtained by [5] and outperforms various other proposed methods. Code is available online 1 .

The retinal periphery of nine healthy subjects was stimulated with computer-generated random-dot kinematograms. These stimuli provided almost isolated visual motion information and minimal position cues. Pattern-reversal stimuli at the same location in the visual field were used for control. Stimulus-related electrical brain activity was recorded from 29 scalp electrodes. Total mean and individual data were analyzed with a spatiotemporal multiple dipole model. The scalp potentials showed a different spatial distribution for motion and pattern stimulation in the time range of 160-200 ms. In this epoch, the predominant motion-related source activity was localized in the region of the contralateral occipital-temporal-parietal border. A significant ipsilateral source activity was not found. The predominant source activity related to the pattern stimulus occurred in the same epoch. The corresponding equivalent dipole was localized more medially and deeper in the brain. The orientation of these major dipole activities was markedly different. These dipoles appeared to represent activity of distinct extrastriate areas, in contrast to earlier activity which was modelled by more posterior dipoles in the occipital lobe. The latter dipoles were at comparable contralateral locations and had similar peak activities around 100 ms, suggesting an origin in the striate cortex.

Pavlovian conditioning of behavioral, but not of endocrine, indicators was effective in susceptible subjects using a rotating chair as US and a single CS-US pairing.