Face morphing is a problem in computer graphics with numerous artistic and forensic applications. It is challenging due to variations in pose, lighting, gender, and ethnicity. This task consists of a warping for feature alignment and a blending for a seamless transition between the warped images. We propose to leverage coord-based neural networks to represent such warpings and blendings of face images. During training, we exploit the smoothness and flexibility of such networks by combining energy functionals employed in classical approaches without discretizations. Additionally, our method is time-dependent, allowing a continuous warping/blending of the images. During morphing inference, we need both direct and inverse transformations of the time-dependent warping. The first (second) is responsible for warping the target (source) image into the source (target) image. Our neural warping stores those maps in a single network dismissing the need for inverting them. The results of our experiments indicate that our method is competitive with both classical and generative models under the lens of image quality and face-morphing detectors. Aesthetically, the resulting images present a seamless blending of diverse faces not yet usual in the literature.

This work investigates the use of smooth neural networks for modeling dynamic variations of implicit surfaces under the level set equation (LSE). For this, it extends the representation of neural implicit surfaces to the space-time ℝ3× ℝ, which opens up mechanisms for continuous geometric transformations. Examples include evolving an initial surface towards general vector fields, smoothing and sharpening using the mean curvature equation, and interpolations of initial conditions.The network training considers two constraints. A data term is responsible for fitting the initial condition to the corresponding time instant, usually ℝ3× {0}. Then, a LSE term forces the network to approximate the underlying geometric evolution given by the LSE, without any supervision. The network can also be initialized based on previously trained initial conditions, resulting in faster convergence compared to the standard approach.

This paper presents a system for immersive visualization of the Classical Non-Euclidean spaces using real-time ray tracing. It exploits the capabilities of the latest generation of GPU's based on the NVIDIA's Turing architecture in order to develop new methods for intuitive exploration of landscapes featuring non-trivial geometry and topology in virtual reality.

SUMMARY: Copy number variation (CNV) is a major type of structural genomic variation that is increasingly studied across different species for association with diseases and production traits. Established protocols for experimental detection and computational inference of CNVs from SNP array and next-generation sequencing data are available. We present the CNVRanger R/Bioconductor package which implements a comprehensive toolbox for structured downstream analysis of CNVs. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and genome-wide association analysis with gene expression and quantitative phenotypes. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/CNVRanger.