Reconstruction and rendering of time-varying natural phenomena

While computer performance increases and computer generated images get ever more realistic, the need for modeling computer graphics content is becoming stronger. To achieve photo-realism detailed scenes have to be modeled often with a significant amount of manual labour. Interdisciplinary research combining the fields of Computer Graphics, Computer Vision and Scientific Computing has led to the development of (semi-)automatic modeling tools freeing the user of labour-intensive modeling tasks. The modeling of animated content is especially challenging. Realistic motion is necessary to convince the audience of computer games, movies with mixed reality content and augmented reality applications. The goal of this thesis is to investigate automated modeling techniques for time-varying natural phenomena. The results of the presented methods are animated, three-dimensional computer models of fire, smoke and fluid flows.Durch die steigende Rechenkapazität moderner Computer besteht die Möglichkeit immer realistischere Bilder virtuell zu erzeugen. Dadurch entsteht ein größerer Bedarf an Modellierungsarbeit um die nötigen Objekte virtuell zu beschreiben. Um photorealistische Bilder erzeugen zu können müssen sehr detaillierte Szenen, oft in mühsamer Handarbeit, modelliert werden. Ein interdisziplinärer Forschungszweig, der Computergrafik, Bildverarbeitung und Wissenschaftliches Rechnen verbindet, hat in den letzten Jahren die Entwicklung von (semi-)automatischen Methoden zur Modellierung von Computergrafikinhalten vorangetrieben. Die Modellierung dynamischer Inhalte ist dabei eine besonders anspruchsvolle Aufgabe, da realistische Bewegungsabläufe sehr wichtig für eine überzeugende Darstellung von Computergrafikinhalten in Filmen, Computerspielen oder Augmented-Reality Anwendungen sind. Das Ziel dieser Arbeit ist es automatische Modellierungsmethoden für dynamische Naturerscheinungen wie Wasserfluss, Feuer, Rauch und die Bewegung erhitzter Luft zu entwickeln. Das Resultat der entwickelten Methoden sind dabei dynamische, dreidimensionale Computergrafikmodelle

Shape: A 3D Modeling Tool for Astrophysics

We present a flexible interactive 3D morpho-kinematical modeling application for astrophysics. Compared to other systems, our application reduces the restrictions on the physical assumptions, data type and amount that is required for a reconstruction of an object's morphology. It is one of the first publicly available tools to apply interactive graphics to astrophysical modeling. The tool allows astrophysicists to provide a-priori knowledge about the object by interactively defining 3D structural elements. By direct comparison of model prediction with observational data, model parameters can then be automatically optimized to fit the observation. The tool has already been successfully used in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE Transactions on Visualization and Computer Graphics

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

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.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019), additional materials: http://www.byungsoo.me/project/deep-fluids

Single-image Tomography: 3D Volumes from 2D Cranial X-Rays

As many different 3D volumes could produce the same 2D x-ray image, inverting this process is challenging. We show that recent deep learning-based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which is then fused in a second step with the input x-ray into a high-resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer-simulated 2D x-ray images of 3D volumes scanned from 175 mammalian species. Applications of our approach include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x-rays

Single-picture reconstruction and rendering of trees for plausible vegetation synthesis

State-of-the-art approaches for tree reconstruction either put limiting constraints on the input side (requiring multiple photographs, a scanned point cloud or intensive user input) or provide a representation only suitable for front views of the tree. In this paper we present a complete pipeline for synthesizing and rendering detailed trees from a single photograph with minimal user effort. Since the overall shape and appearance of each tree is recovered from a single photograph of the tree crown, artists can benefit from georeferenced images to populate landscapes with native tree species. A key element of our approach is a compact representation of dense tree crowns through a radial distance map. Our first contribution is an automatic algorithm for generating such representations from a single exemplar image of a tree. We create a rough estimate of the crown shape by solving a thin-plate energy minimization problem, and then add detail through a simplified shape-from-shading approach. The use of seamless texture synthesis results in an image-based representation that can be rendered from arbitrary view directions at different levels of detail. Distant trees benefit from an output-sensitive algorithm inspired on relief mapping. For close-up trees we use a billboard cloud where leaflets are distributed inside the crown shape through a space colonization algorithm. In both cases our representation ensures efficient preservation of the crown shape. Major benefits of our approach include: it recovers the overall shape from a single tree image, involves no tree modeling knowledge and minimal authoring effort, and the associated image-based representation is easy to compress and thus suitable for network streaming.Peer ReviewedPostprint (author's final draft

Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences

Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions. Collecting and annotating such a dataset is an approach that cannot scale to sufficient variety and realism. We free ourselves from this limitation by training on unannotated images. Our method leverages the observation that two images of the same scene but with different lighting provide useful information on their intrinsic properties: by definition, albedo is invariant to lighting conditions, and cross-combining the estimated albedo of a first image with the estimated shading of a second one should lead back to the second one's input image. We transcribe this relationship into a siamese training scheme for a deep convolutional neural network that decomposes a single image into albedo and shading. The siamese setting allows us to introduce a new loss function including such cross-combinations, and to train solely on (time-lapse) images, discarding the need for any ground truth annotations. As a result, our method has the good properties of i) taking advantage of the time-varying information of image sequences in the (pre-computed) training step, ii) not requiring ground truth data to train on, and iii) being able to decompose single images of unseen scenes at runtime. To demonstrate and evaluate our work, we additionally propose a new rendered dataset containing illumination-varying scenes and a set of quantitative metrics to evaluate SIID algorithms. Despite its unsupervised nature, our results compete with state of the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201

Compressed Sensing - A New mode of Measurement

After introducing the concept of compressed sensing as a complementary measurement mode to the classical Shannon-Nyquist approach, I discuss some of the drivers, potential challenges and obstacles to its implementation. I end with a speculative attempt to embed compressed sensing as an enabling methodology within the emergence of data-driven discovery. As a consequence I predict the growth of non-nomological sciences where heuristic correlations will find applications but often bypass conventional pure basic and use-inspired basic research stages due to the lack of verifiable hypotheses

Hydrodynamics of a 5D Einstein-dilaton black hole solution and the corresponding BPS state

We apply the potential reconstruction approach to generate a series of asymptotically AdS (aAdS) black hole solutions, with a self-interacting bulk scalar field. Based on the method, we reproduce the pure AdS solution as a consistency check and we also generate a simple analytic 5D black hole solution. We then study various aspects of this solution, such as temperature, entropy density and conserved charges. Furthermore, we study the hydrodynamics of this black hole solution in the framework of fluid/gravity duality, e.g. the ratio of the shear viscosity to the entropy density. In a degenerate case of the 5D black hole solution, we find that the c function decreases monotonically from UV to IR as expected. Finally, we investigate the stability of the degenerate solution by studying the bosonic functional energy of the gravity and the Witten-Nester energy $E_{WN}$. We confirm that the degenerate solution is a BPS domain wall solution. The corresponding superpotential and the solution of the killing spinor equation are found explicitly.Comment: V2: 23 pages, no figure, minor changes, typos corrected, new references and comments added, version accepted by JHE