EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment

Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their applications in mobile and outdoor environments. We present EgoFace, a radically new lightweight setup for face performance capture and front-view videorealistic reenactment using a single egocentric RGB camera. Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments. The input image is projected into a low dimensional latent space of the facial expression parameters. Through careful adversarial training of the parameter-space synthetic rendering, a videorealistic animation is produced. Our problem is challenging as the human visual system is sensitive to the smallest face irregularities that could occur in the final results. This sensitivity is even stronger for video results. Our solution is trained in a pre-processing stage, through a supervised manner without manual annotations. EgoFace captures a wide variety of facial expressions, including mouth movements and asymmetrical expressions. It works under varying illuminations, background, movements, handles people from different ethnicities and can operate in real time

GVP: Generative Volumetric Primitives

Advances in 3D-aware generative models have pushed the boundary of image synthesis with explicit camera control. To achieve high-resolution image synthesis, several attempts have been made to design efficient generators, such as hybrid architectures with both 3D and 2D components. However, such a design compromises multiview consistency, and the design of a pure 3D generator with high resolution is still an open problem. In this work, we present Generative Volumetric Primitives (GVP), the first pure 3D generative model that can sample and render 512-resolution images in real-time. GVP jointly models a number of volumetric primitives and their spatial information, both of which can be efficiently generated via a 2D convolutional network. The mixture of these primitives naturally captures the sparsity and correspondence in the 3D volume. The training of such a generator with a high degree of freedom is made possible through a knowledge distillation technique. Experiments on several datasets demonstrate superior efficiency and 3D consistency of GVP over the state-of-the-art.Comment: https://vcai.mpi-inf.mpg.de/projects/GVP/index.htm

Laparoscopic Approach to Vesicovaginal Fistula: Our Experience

AbstractIntroduction: Most Vesicovaginal fistulas  in the industrialized world are iatrogenic, Though they may also result from congenital anomalies, malignant disease, inflammation and infection, radiation therapy, iatrogenic (surgical) or external tissue trauma, ischemia, parturition and a variety of other processes. Vesicovaginal fistulas (VVF) represent, by far, the most common type of acquired fistula of the urinary tract. The goal of treatment of these fistulas is the rapid cessation of urine leakage with return of normal and complete urinary and genital function.Materials and Methods: Female patients presenting with iatrogenic Vesicovaginal fistula formed the study group. A detailed history and physical examination was carried out. Imaging included intravenous urogram, cystogram, computerised tomography, MR imaging and retrograde ureterogram as felt necessary.  Surgical repair of Vesicovaginal fistula was carried out through a laparoscopic approach.Results: 24 women presented with VVF, of these 19 underwent laparoscopic transperitoneal repair, whereas 5 underwent laparoscopic transvesicoscopic repair. The intraoperative blood loss was minimal (< 100 ml) and no major perioperative complications were noted. Conclusions: Minimally invasive approaches to repair vesico-vaginal fistulas are feasible, safe and associated with minimal blood loss, hospital stay and morbidity.Keywords: Laparoscopy, Minimally invasive, Vesicovaginal fistul

Learning Complete {3D} Morphable Face Models from Images and Videos

Most 3D face reconstruction methods rely on 3D morphable models, which disentangle the space of facial deformations into identity geometry, expressions and skin reflectance. These models are typically learned from a limited number of 3D scans and thus do not generalize well across different identities and expressions. We present the first approach to learn complete 3D models of face identity geometry, albedo and expression just from images and videos. The virtually endless collection of such data, in combination with our self-supervised learning-based approach allows for learning face models that generalize beyond the span of existing approaches. Our network design and loss functions ensure a disentangled parameterization of not only identity and albedo, but also, for the first time, an expression basis. Our method also allows for in-the-wild monocular reconstruction at test time. We show that our learned models better generalize and lead to higher quality image-based reconstructions than existing approaches

LiveHand: Real-time and Photorealistic Neural Hand Rendering

The human hand is the main medium through which we interact with our surroundings. Hence, its digitization is of uttermost importance, with direct applications in VR/AR, gaming, and media production amongst other areas. While there are several works for modeling the geometry and articulations of hands, little attention has been dedicated to capturing photo-realistic appearance. In addition, for applications in extended reality and gaming, real-time rendering is critical. In this work, we present the first neural-implicit approach to photo-realistically render hands in real-time. This is a challenging problem as hands are textured and undergo strong articulations with various pose-dependent effects. However, we show that this can be achieved through our carefully designed method. This includes training on a low-resolution rendering of a neural radiance field, together with a 3D-consistent super-resolution module and mesh-guided space canonicalization and sampling. In addition, we show the novel application of a perceptual loss on the image space is critical for achieving photorealism. We show rendering results for several identities, and demonstrate that our method captures pose- and view-dependent appearance effects. We also show a live demo of our method where we photo-realistically render the human hand in real-time for the first time in literature. We ablate all our design choices and show that our design optimizes for both photorealism and rendering speed. Our code will be released to encourage further research in this area.Comment: 11 pages, 8 figure