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Progressive 3D Scene Understanding with Stacked Neural Networks
(The Eurographics Association, 2018) Song, Youcheng; Sun, Zhengxing; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
3D scene understanding is difficult due to the natural hierarchical structures and complicated contextual relationships in the 3d scenes. In this paper, a progressive 3D scene understanding method is proposed. The scene understanding task is decomposed into several different but related tasks, and semantic objects are progressively separated from coarse to fine. It is achieved by stacking multiple segmentation networks. The former network segments the 3D scene at a coarser level and passes the result as context to the latter one for a finer-grained segmentation. For the network training, we build a connection graph (vertices indicating objects and edges' weights indicating contact area between objects), and calculate a maximum spanning tree to generate coarse-to-fine labels. Then we train the stacked network by hierarchical supervision based on the generated coarseto- fine labels. Finally, using the trained model, we can not only obtain better segmentation accuracy at the finest-grained than directly using the segmentation network, but also obtain a hierarchical understanding result of the 3d scene as a bonus.
Depth-Based Face Recognition by Learning from 3D-LBP Images
(The Eurographics Association, 2019) Neto, Joao Baptista Cardia; Marana, Aparecido Nilceu; Ferrari, Claudio; Berretti, Stefano; Bimbo, Alberto Del; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
In this paper, we propose a hybrid framework for face recognition from depth images, which is both effective and efficient. It consists of two main stages: First, the 3DLBP operator is applied to the raw depth data of the face, and used to build the corresponding descriptor images (DIs). However, such operator quantizes relative depth differences over/under +-7 to the same bin, so as to generate a fixed dimensional descriptor. To account for this behavior, we also propose a modification of the traditional operator that encodes depth differences using a sigmoid function. Then, a not-so-deep (shallow) convolutional neural network (SCNN) has been designed that learns from the DIs. This architecture showed two main advantages over the direct application of deep-CNN (DCNN) to depth images of the face: On the one hand, the DIs are capable of enriching the raw depth data, emphasizing relevant traits of the face, while reducing their acquisition noise. This resulted decisive in improving the learning capability of the network; On the other, the DIs capture low-level features of the face, thus playing the role for the SCNN as the first layers do in a DCNN architecture. In this way, the SCNN we have designed has much less layers and can be trained more easily and faster. Extensive experiments on low- and high-resolution depth face datasets confirmed us the above advantages, showing results that are comparable or superior to the state-of-the-art, using by far less training data, time, and memory occupancy of the network.
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Kim, Byungsoo; Azevedo, Vinicius C.; Thuerey, Nils; Kim, Theodore; Gross, Markus; Solenthaler, Barbara; Alliez, Pierre and Pellacini, Fabio
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300x.
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Marnerides, Demetris; Bashford-Rogers, Thomas; Hatchett, Jon; Debattista, Kurt; Gutierrez, Diego and Sheffer, Alla
High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.
Learning-Based Animation of Clothing for Virtual Try-On
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Santesteban, Igor; Otaduy, Miguel A.; Casas, Dan; Alliez, Pierre and Pellacini, Fabio
This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning-based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try-on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results.
A CNN-based Flow Correction Method for Fast Preview
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Xiao, Xiangyun; Wang, Hui; Yang, Xubo; Alliez, Pierre and Pellacini, Fabio
Eulerian-based smoke simulations are sensitive to the initial parameters and grid resolutions. Due to the numerical dissipation on different levels of the grid and the nonlinearity of the governing equations, the differences in simulation resolutions will result in different results. This makes it challenging for artists to preview the animation results based on low-resolution simulations. In this paper, we propose a learning-based flow correction method for fast previewing based on low-resolution smoke simulations. The main components of our approach lie in a deep convolutional neural network, a grid-layer feature vector and a special loss function. We provide a novel matching model to represent the relationship between low-resolution and high-resolution smoke simulations and correct the overall shape of a low-resolution simulation to closely follow the shape of a high-resolution down-sampled version. We introduce the grid-layer concept to effectively represent the 3D fluid shape, which can also reduce the input and output dimensions. We design a special loss function for the fluid divergence-free constraint in the neural network training process. We have demonstrated the efficacy and the generality of our approach by simulating a diversity of animations deviating from the original training set. In addition, we have integrated our approach into an existing fluid simulation framework to showcase its wide applications.
Controlling Stroke Size in Fast Style Transfer with Recurrent Convolutional Neural Network
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Yang, Lingchen; Yang, Lumin; Zhao, Mingbo; Zheng, Youyi; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Controlling stroke size in Fast Style Transfer remains a difficult task. So far, only a few attempts have been made towards it, and they still exhibit several deficiencies regarding efficiency, flexibility, and diversity. In this paper, we aim to tackle these problems and propose a recurrent convolutional neural subnetwork, which we call recurrent stroke-pyramid, to control the stroke size in Fast Style Transfer. Compared to the state-of-the-art methods, our method not only achieves competitive results with much fewer parameters but provides more flexibility and efficiency for generalizing to unseen larger stroke size and being able to produce a wide range of stroke sizes with only one residual unit. We further embed the recurrent stroke-pyramid into the Multi-Styles and the Arbitrary-Style models, achieving both style and stroke-size control in an entirely feed-forward manner with two novel run-time control strategies.
Aging Prediction of Cultural Heritage Samples Based on Surface Microgeometry
(The Eurographics Association, 2018) Ciortan, Irina Mihaela; Marchioro, Giacomo; Daffara, Claudia; Pintus, Ruggero; Gobbetti, Enrico; Giachetti, Andrea; Sablatnig, Robert and Wimmer, Michael
A critical and challenging aspect for the study of Cultural Heritage (CH) assets is related to the characterization of the materials that compose them and to the variation of these materials with time. In this paper, we exploit a realistic dataset of artificially aged metallic samples treated with different coatings commonly used for artworks' protection in order to evaluate different approaches to extract material features from high-resolution depth maps. In particular, we estimated, on microprofilometric surface acquisitions of the samples, performed at different aging steps, standard roughness descriptors used in materials science as well as classical and recent image texture descriptors. We analyzed the ability of the features to discriminate different aging steps and performed supervised classification tests showing the feasibility of a texture-based aging analysis and the effectiveness of coatings in reducing the surfaces' change with time.
Learned Fitting of Spatially Varying BRDFs
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Merzbach, Sebastian; Hermann, Max; Rump, Martin; Klein, Reinhard; Boubekeur, Tamy and Sen, Pradeep
The use of spatially varying reflectance models (SVBRDF) is the state of the art in physically based rendering and the ultimate goal is to acquire them from real world samples. Recently several promising deep learning approaches have emerged that create such models from a few uncalibrated photos, after being trained on synthetic SVBRDF datasets. While the achieved results are already very impressive, the reconstruction accuracy that is achieved by these approaches is still far from that of specialized devices. On the other hand, fitting SVBRDF parameter maps to the gibabytes of calibrated HDR images per material acquired by state of the art high quality material scanners takes on the order of several hours for realistic spatial resolutions. In this paper, we present a first deep learning approach that is capable of producing SVBRDF parameter maps more than two orders of magnitude faster than state of the art approaches, while still providing results of equal quality and generalizing to new materials unseen during the training. This is made possible by training our network on a large-scale database of material scans that we have gathered with a commercially available SVBRDF scanner. In particular, we train a convolutional neural network to map calibrated input images to the 13 parameter maps of an anisotropic Ward BRDF, modified to account for Fresnel reflections, and evaluate the results by comparing the measured images against re-renderings from our SVBRDF predictions. The novel approach is extensively validated on real world data taken from our material database, which we make publicly available under https://cg.cs.uni-bonn.de/svbrdfs/.
Projecting our Past to the Future - Challenges and Results: The Case of Asinou church
(The Eurographics Association, 2017) Papaefthymiou, M.; Nikolakopoulou, V.; Leventis, Georgios; Domajnko, M.; Ioannides, M.; Papagiannakis, G.; Santos, P.; Tobias Schreck and Tim Weyrich and Robert Sablatnig and Benjamin Stular
In this paper, we present some of the novel results of the Marie Curie Initial Training Network for Digital Cultural Heritage (ITN-DCH) project, describing briefly the work done focusing on the project's first case study: the Panagia Phorviotisa of Asinou, an UNESCO World Heritage Listed (WHL) monument in Cyprus. The paper introduces some challenges and the importance of multidisciplinary, sustainable research and development in the emerging domain of DCH in Europe. The different methodologies address these challenges through a professional network of partners including Academia, Research and Industry. The paper describes the 3D documentation of the church and how the data acquired can be used and re-used in Mixed Reality (MR) applications using also Deep Learning techniques, as well as in Education.

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