Appearance and Pose-Conditioned Human Image Generation Using Deformable GANs.

In this paper, we address the problem of generating person images conditioned on both pose and appearance information. Specifically, given an image x of a person and a target pose P(x), extracted from an image x, we synthesize a new image of that person in pose P(x), while preserving the visual details in x. In order to deal with pixel-to-pixel misalignments caused by the pose differences between P(x) and P(x), we introduce deformable skip connections in the generator of our Generative Adversarial Network.

Progressive Fusion for Unsupervised Binocular Depth Estimation Using Cycled Networks.

Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps.

A Comprehensive Analysis of Deep Regression.

Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, together with the stochastic nature of the optimization procedures, produce notably different results, making extremely difficult to sift methods that significantly outperform others.