3D Face Reconstruction from 2D Pictures: First Results of a Web-Based Computer Aided System for Aesthetic Procedures

The human face is a vital component of our identity and many people undergo medical aesthetics procedures in order to achieve an ideal or desired look. However, communication between physician and patient is fundamental to understand the patient’s wishes and to achieve the desired results. To date, most plastic surgeons rely on either “free hand” 2D drawings on picture printouts or computerized picture morphing. Alternatively, hardware dependent solutions allow facial shapes to be created and planned in 3D, but they are usually expensive or complex to handle. To offer a simple and hardware independent solution, we propose a web-based application that uses 3 standard 2D pictures to create a 3D representation of the patient’s face on which facial aesthetic procedures such as filling, skin clearing or rejuvenation, and rhinoplasty are planned in 3D. The proposed application couples a set of well-established methods together in a novel manner to optimize 3D reconstructions for clinical use. Face reconstructions performed with the application were evaluated by two plastic surgeons and also compared to ground truth data. Results showed the application can provide accurate 3D face representations to be used in clinics (within an average of 2 mm error) in less than 5 min.

Cell-cell fusion induced by the Ig3 domain of receptor FGFRL1 in CHO cells.

FGFRL1 is a single-pass transmembrane protein with three extracellular Ig domains. When overexpressed in CHO cells or related cell types, it induces cell-cell fusion and formation of large, multinucleated syncytia. For this fusion-promoting activity, only the membrane-proximal Ig domain (Ig3) and the transmembrane domain are required. It does not matter whether the transmembrane domain is derived from FGFRL1 or from another receptor, but the distance of the Ig3 domain to the membrane is crucial. Fusion can be inhibited with soluble recombinant proteins comprising the Ig1-Ig2-Ig3 or the Ig2-Ig3 domains as well as with monoclonal antibodies directed against Ig3. Mutational analysis reveals a hydrophobic site in Ig3 that is required for fusion. If a single amino acid from this site is mutated, fusion is abolished. The site is located on a β-sheet, which is part of a larger β-barrel, as predicted by computer modeling of the 3D structure of FGFRL1. It is possible that this site interacts with a target protein of neighboring cells to trigger cell-cell fusion.

Robust Denoising using Feature and Color Information

We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising filters because features are highly correlated with rendered images. Filters based solely on features, however, are prone to blurring image details that are not well represented by the features. On the other hand, color buffers represent all details, but they may be less effective to determine filters because they are contaminated by the noise that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.

Optimizing stereo-to-multiview conversion for autostereoscopic displays

We present a novel stereo-to-multiview video conversion method for glasses-free multiview displays. Different from previous stereo-to-multiview approaches, our mapping algorithm utilizes the limited depth range of autostereoscopic displays optimally and strives to preserve the scene's artistic composition and perceived depth even under strong depth compression. We first present an investigation of how perceived image quality relates to spatial frequency and disparity. The outcome of this study is utilized in a two-step mapping algorithm, where we (i) compress the scene depth using a non-linear global function to the depth range of an autostereoscopic display and (ii) enhance the depth gradients of salient objects to restore the perceived depth and salient scene structure. Finally, an adapted image domain warping algorithm is proposed to generate the multiview output, which enables overall disparity range extension.

Interactive Diffraction from Biological Nanostructures

We describe a technique for interactive rendering of diffraction effects produced by biological nanostructures, such as snake skin surface gratings. Our approach uses imagery from atomic force microscopy that accurately captures the geometry of the nanostructures responsible for structural colouration, that is, colouration due to wave interference, in a variety of animals. We develop a rendering technique that constructs bidirectional reflection distribution functions (BRDFs) directly from the measured data and leverages pre-computation to achieve interactive performance. We demonstrate results of our approach using various shapes of the surface grating nanostructures. Finally, we evaluate the accuracy of our pre-computation-based technique and compare to a reference BRDF construction technique.

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.