Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks

3D Morphable Model (3DMM) based methods have achieved great success in recovering 3D face shapes from single-view images. However, the facial textures recovered by such methods lack the fidelity as exhibited in the input images. Recent work demonstrates high-quality facial texture recovering with generative networks trained from a large-scale database of high-resolution UV maps of face textures, which is hard to prepare and not publicly available. In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database. The main idea is to refine the initial texture generated by a 3DMM based method with facial details from the input image. To this end, we propose to use graph convolutional networks to reconstruct the detailed colors for the mesh vertices instead of reconstructing the UV map. Experiments show that our method can generate high-quality results and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.Comment: Accepted to CVPR 2020. The source code is available at https://github.com/FuxiCV/3D-Face-GCN

Morphable Face Models - An Open Framework

In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration and model-building, demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model

09251 Abstracts Collection -- Scientific Visualization

From 06-14-2009 to 06-19-2009, the Dagstuhl Seminar 09251 ``Scientific Visualization \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, over 50 international participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

07291 Abstracts Collection -- Scientific Visualization

From 15.07. to 20.07.07, the Dagstuhl Seminar 07291 ``Scientific Visualization\u27\u27 was held in the International Conference and Research Center (IBFI),Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

A Brief Survey of Image-Based Depth Upsampling

Recently, there has been remarkable growth of interest in the development and applications of Time-of-Flight (ToF) depth cameras. However, despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we compare ToF cameras to three image-based techniques for depth recovery, discuss the upsampling problem and survey the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also mentioned

Web-based visualization for 3D data in archaeology : The ADS 3D viewer

The solid geometry of archaeological deposits is fundamental to the interpretation of their chronological sequence. However, such stratigraphic sequences are generally viewed as static two-dimensional diagrammatic representations which are difficult to manipulate or to relate to real layers. The ADS 3D Viewer is a web-based resource for the management and analysis of archaeological data. The viewer was developed to take advantage of recent developments in web technology, namely the adoption of WebGL (Web Graphics Library) by current web browsers. The ADS 3D Viewer combines the potential of the 3D Heritage Online Presenter (3DHOP), a software package for the web-based visualization of 3D geometries, with the infrastructure of the Archaeology Data Service (ADS) repository, in the attempt to create a platform for the visualization and analysis of 3D data archived by the ADS. Two versions of the viewer have been developed to answer the needs of different users. The first version, the Object Level 3D Viewer, was implemented to extend the browsing capability of ADS project archives by enabling the visualization of single 3D models. The second version, the Stratigraphy 3D Viewer, is an extension which allows the exploration of a specific kind of aggregated data: the multiple layers of an archaeological stratigraphic sequence. This allows those unable to participate directly in the fieldwork to access, analyse and re-interpret the archaeological context remotely. This has the potential to transform the discipline, allowing inter-disciplinary, cross-border and ‘at-distance’ collaborative workflows, and enabling easier access to and analysis of archaeological data