In the last few years, several works have tackled the problem of novel view synthesis from stereo images or even from a single picture. However, previous methods are computationally expensive, specially for high-resolution images. In this paper, we address the problem of generating a multiplane image (MPI) from a single high-resolution picture. We present the adaptive-MPI representation, which allows rendering novel views with low computational requirements. To this end, we propose an adaptive slicing algorithm that produces an MPI with a variable number of image planes. We present a new lightweight CNN for depth estimation, which is learned by knowledge distillation from a larger network. Occluded regions in the adaptive-MPI are inpainted also by a lightweight CNN. We show that our method is capable of producing high-quality predictions with one order of magnitude less parameters compared to previous approaches. The robustness of our method is evidenced on challenging pictures from the Internet.

Stereo vision is a growing topic in computer vision due to the innumerable opportunities and applications this technology offers for the development of modern solutions, such as virtual and augmented reality applications. To enhance the user's experience in three-dimensional virtual environments, the motion parallax estimation is a promising technique to achieve this objective. In this paper, we propose an algorithm for generating parallax motion effects from a single image, taking advantage of state-of-the-art instance segmentation and depth estimation approaches. This work also presents a comparison against such algorithms to investigate the trade-off between efficiency and quality of the parallax motion effects, taking into consideration a multi-task learning network capable of estimating instance segmentation and depth estimation at once. Experimental results and visual quality assessment indicate that the PyD-Net network (depth estimation) combined with Mask R-CNN or FBNet networks (instance segmentation) can produce parallax motion effects with good visual quality.

Recent deep learning techniques have achieved satisfactory results for various image-related problems. However, many research questions remain open in tasks involving video sequences. Several applications demand the understanding of complex events in videos, such as traffic monitoring, person re-identification, security and surveillance. In this work, we address the problem of human action recognition in videos through a multi-stream network that incorporates both spatial and temporal information. The main contribution of our work is a stream based on a new variant of the visual rhythm, called Learnable Visual Rhythm (LVR). We employ a deep network to extract features from the video frames in order to generate the rhythm. The features are collected at multiple depths of the network to enable the analysis of different abstraction levels. This strategy significantly outperforms the handcrafted version on the UCF101 and HMDB51 datasets. Experiments conducted on these datasets show that our final multi-stream network achieved competitive results compared to state-of-the-art approaches.

A large amount of video content has been produced by compact and portable cameras. Several applications have been benefited from such growth of multimedia data, such as telemedicine, business conferencing, surveillance and security, entertainment, distance learning, and robotics. Video stabilisation is the process of detecting and removing undesired motion or instabilities from a video stream caused during the acquisition stage when handling the camera. In this work, the authors introduce and analyse a novel approach that identifies failures in the global motion estimation of the camera by means of local features. Moreover, they propose an optimisation method for computing a new estimate of the corrected motion. Experiments conducted on different video sequences are performed to demonstrate the effectiveness of the developed method. Results obtained with the stabilisation process are compared against the state‐of‐the‐art YouTube method.