bluuebunny/crossref_metadata_2025 huggingface dataset:
Reconstructing the terrestrial flora and marine plankton of the Middle Devonian of Spain: implications for biotic interchange and palaeogeography
Authors: Askew A. J. , Wellman C. H.
Published: November 2019

A rich and well-preserved palynomorph assemblage from the Middle Devonian of northern Spain is analysed with regard to palaeobiogeography and palaeocontinental reconstruction. The communities of terrestrial plants (dispersed spores), marine zooplankton (chitinozoans) and marine phytoplankton (acritarchs and prasinophytes) all show significant endemism. They are depauperate in some respects, missing common species found elsewhere, but also containing many endemic taxa. Measures of similarity and cluster analysis reveal little relatedness to other contemporary assemblages, with both the spore and phytoplankton communities being sister groups to all other communities except one. Attempted correlation with local, regional and global sea-level and transgression–regression schemes is complicated by the absence of key index palynomorphs in this unusual assemblage. The distinctive sedimentary sequence of northern Spain may reflect an increased clastic input to the marine shelf resulting from an increasingly monsoonal climate, possibly connected to the Kačák extinction event. The assemblage's unusual, endemic character requires restrictions on dispersal. The terrestrial spore assemblage suggests that large tracts of ocean existed, without appreciable land bridges, between the Armorican Terrane Assemblage and Laurussia to the north and Gondwana to the south. The phytoplankton communities support existing evidence for significant east to west ocean currents through the Rheic Ocean.

Supplementary material:
Supplementary Tables 1–3 contain presence–absence data for spores, chitinozoans and phytoplankton respectively, in various assemblages, used to construct the dendrograms in Figures 4–6. The tables are available at
https://doi.org/10.6084/m9.figshare.c.4726862
Environmental variability in response to abrupt climatic change during the Last Glacial–Interglacial Transition (16–8 cal ka BP): evidence from Mainland, Orkney
Authors: Abrook Ashley M. , Matthews Ian P. , Milner Alice M. , Candy Ian , Palmer Adrian P. , Timms Rhys G. O.
Published: November 2019

The Last Glacial–Interglacial Transition (LGIT) is a period of climatic complexity where millennial-scale climatic reorganization led to changes in ecosystems. Alongside millennial-scale changes, centennial-scale climatic events have been observed within records from Greenland and continental Europe. The effects of these abrupt events on landscapes and environments are difficult to discern at present. This, in part, relates to low temporal resolutions attained by many studies and the sensitivity of palaeoenvironmental proxies to abrupt change. We present a high-resolution palynological and charcoal study of Quoyloo Meadow, Orkney and use the Principal Curve statistical method to assist in revealing biostratigraphic change. The LGIT vegetation succession on Orkney is presented as open grassland and
Empetrum
heath during the Windermere Interstadial and early Holocene, and open grassland with
Artemisia
during the Loch Lomond Stadial. However, a further three phases of ecological change, characterized by expansions of open ground flora, are dated to 14.05–13.63, 10.94–10.8 and 10.2 cal ka BP. The timing of these changes is constrained by cryptotephra of known age. The paper concludes by comparing Quoyloo Meadow with Crudale Meadow, Orkney, and suggests that both Windermere Interstadial records are incomplete and that fire is an important landscape control during the early Holocene.

Supplementary material
: All raw data associated with this publication: raw pollen counts, charcoal data, Principal Curve and Rate of Change outputs and the age-model output are available at
https://doi.org/10.6084/m9.figshare.c.4725269

Thematic collection:
This article is part of the ‘Early Career Research’ available at:
https://www.lyellcollection.org/cc/SJG-early-career-research
Cenomanian–Turonian drowning of the Arabian Carbonate Platform, the İnişdere section, Adıyaman, SE Turkey
Authors: Mulayim O. , Yilmaz O. I. , Sarı B. , Tasli K. , Wagreich M.
Published: November 2019

The Cenomanian–Turonian carbonate ramp in the Adıyaman Region of SE Turkey (Northern Arabian Platform) records an abrupt shift from benthic carbonate deposits to pelagic deposits near the Cenomanian–Turonian boundary event (CTBE) in the İnişdere stratigraphic section and surrounding borehole sections. A positive δ
13
C excursion of up to 2.15% is recorded in carbonate and organic carbon deposited around the CTBE and provides evidence of a direct link between the CTBE and oceanic anoxic events and the demise of the shallow carbonate production in the Derdere Formation. The microfacies analyses, biostratigraphic dating and palaeoenvironmental interpretations suggest that the platform was drowned near the CTBE as a result of changing environmental conditions. The microfacies indicating significant deepening show a contemporaneity to equivalent surfaces globally and thus strongly support an isochronous formation of Cenomanian–Turonian facies by eustatic sea-level changes. Anoxia spreading over the platform drastically reduced the carbonate production as observed in the studied sections and, therefore, resulted in a reduction in carbonate accumulation rates. Regional/local subsidence and a coeval sea-level rise during the late Cenomanian to early Turonian interval were the cause of the drowning of the platform, including regional anoxia at the northern Arabian platform linked to the Cenomanian–Turonian oceanic anoxic event (OAE2).
Towards Digitization of Collaborative Savings Among Low-Income Groups
Authors: Mehmood Hamid , Ahmad Tallal , Razaq Lubna , Mare Shrirang , Usmani Maryem Zafar , Anderson Richard , Raza Agha Ali
Published: November 2019

Rotating Savings and Credit Association (ROSCA) is a mechanism of informal collaborative savings that is widely used across the globe. Despite its popularity and prevalence, it is not well-studied from HCI and CSCW perspectives. The global increase in mobile penetration has created opportunities to serve the unbanked using mobile-based Digital Financial Services (DFS) for greater financial inclusion but there have not been any DFS-based interventions around ROSCAs. In this paper, we report a qualitative study involving 80 individuals to understand the dynamics of ROSCAs and opportunities for their digitization in the Pakistani context. We also present a smartphone-based Digital ROSCA platform designed on top of a simulated mobile money system. The platform was designed to be inclusive towards low-literate users. We present qualitative findings of its evaluation with 15 users (3 individual ROSCA groups). We find that digitization has the potential to support and strengthen traditional ROSCAs by mitigating issues like record-keeping, delayed payments, collection, distribution, and safety of money. It also allows the creation of payment history for individuals that can be used to score their financial credibility.
Design and structural optimization of topological interlocking assemblies
Authors: Wang Ziqi , Song Peng , Isvoranu Florin , Pauly Mark
Published: November 2019

We study assemblies of convex rigid blocks regularly arranged to approximate a given freeform surface. Our designs rely solely on the geometric arrangement of blocks to form a stable assembly, neither requiring explicit connectors or complex joints, nor relying on friction between blocks. The convexity of the blocks simplifies fabrication, as they can be easily cut from different materials such as stone, wood, or foam. However, designing stable assemblies is challenging, since adjacent pairs of blocks are restricted in their relative motion only in the direction orthogonal to a single common planar interface surface. We show that despite this weak interaction, structurally stable, and in some cases, globally interlocking assemblies can be found for a variety of freeform designs. Our optimization algorithm is based on a theoretical link between static equilibrium conditions and a geometric, global interlocking property of the assembly---that an assembly is globally interlocking if and only if the equilibrium conditions are satisfied for arbitrary external forces and torques. Inspired by this connection, we define a measure of stability that spans from single-load equilibrium to global interlocking, motivated by tilt analysis experiments used in structural engineering. We use this measure to optimize the geometry of blocks to achieve a static equilibrium for a maximal cone of directions, as opposed to considering only self-load scenarios with a single gravity direction. In the limit, this optimization can achieve globally interlocking structures. We show how different geometric patterns give rise to a variety of design options and validate our results with physical prototypes.
Reducing simulator sickness with perceptual camera control
Authors: Hu Ping , Sun Qi , Didyk Piotr , Wei Li-Yi , Kaufman Arie E.
Published: November 2019

Virtual-reality provides an immersive environment but can induce cybersickness due to the discrepancy between visual and vestibular cues. To avoid this problem, the movement of the virtual camera needs to match the motion of the user in the real world. Unfortunately, this is usually difficult due to the mismatch between the size of the virtual environments and the space available to the users in the physical domain. The resulting constraints on the camera movement significantly hamper the adoption of virtual-reality headsets in many scenarios and make the design of the virtual environments very challenging. In this work, we study how the characteristics of the virtual camera movement (e.g., translational acceleration and rotational velocity) and the composition of the virtual environment (e.g., scene depth) contribute to perceived discomfort. Based on the results from our user experiments, we devise a computational model for predicting the magnitude of the discomfort for a given scene and camera trajectory. We further apply our model to a new path planning method which optimizes the input motion trajectory to reduce perceptual sickness. We evaluate the effectiveness of our method in improving perceptual comfort in a series of user studies targeting different applications. The results indicate that our method can reduce the perceived discomfort while maintaining the fidelity of the original navigation, and perform better than simpler alternatives.
Learning efficient illumination multiplexing for joint capture of reflectance and shape
Authors: Kang Kaizhang , Xie Cihui , He Chengan , Yi Mingqi , Gu Minyi , Chen Zimin , Zhou Kun , Wu Hongzhi
Published: November 2019

We propose a novel framework that automatically learns the lighting patterns for efficient, joint acquisition of unknown reflectance and shape. The core of our framework is a deep neural network, with a shared linear encoder that directly corresponds to the lighting patterns used in physical acquisition, as well as non-linear decoders that output per-pixel normal and diffuse / specular information from photographs. We exploit the diffuse and normal information from multiple views to reconstruct a detailed 3D shape, and then fit BRDF parameters to the diffuse / specular information, producing texture maps as reflectance results. We demonstrate the effectiveness of the framework with physical objects that vary considerably in reflectance and shape, acquired with as few as 16 ~ 32 lighting patterns that correspond to 7 ~ 15 seconds of per-view acquisition time. Our framework is useful for optimizing the efficiency in both novel and existing setups, as it can automatically adapt to various factors, including the geometry / the lighting layout of the device and the properties of appearance.
Accelerated complex-step finite difference for expedient deformable simulation
Authors: Luo Ran , Xu Weiwei , Shao Tianjia , Xu Hongyi , Yang Yin
Published: November 2019

In deformable simulation, an important computing task is to calculate the gradient and derivative of the strain energy function in order to infer the corresponding internal force and tangent stiffness matrix. The standard numerical routine is the finite difference method, which evaluates the target function multiple times under a small real-valued perturbation. Unfortunately, the subtractive cancellation prevents us from setting this perturbation sufficiently small, and the regular finite difference is doomed for computing problems requiring a high-accuracy derivative evaluation. In this paper, we graft a new finite difference scheme, namely the complex-step finite difference (CSFD), with physics-based animation. CSFD is based on the complex Taylor series expansion, which avoids subtractions in first-order derivative approximation. As a result, one can use a very small perturbation to calculate the numerical derivative that is as accurate as its analytic counterpart. We accelerate the original CSFD method so that it is also as efficient as the analytic derivative. This is achieved by discarding high-order error terms, decoupling real and imaginary calculations, replacing costly functions based on the theory of equivalent infinitesimal, and isolating the propagation of the perturbation in composite/nesting functions. CSFD can be further augmented with multicomplex Taylor expansion and Cauchy-Riemann formula to handle higher-order derivatives and tensor-valued functions. We demonstrate the accuracy, convenience, and efficiency of this new numerical routine in the context of deformable simulation - one can easily deploy a robust simulator for general hyperelastic materials, including user-crafted ones to cater specific needs in different applications. Higher-order derivatives of the energy can be readily computed to construct modal derivative bases for reduced real-time simulation. Inverse simulation problems can also be conveniently solved using gradient/Hessian-based optimization procedures.
LOGAN
Authors: Yin Kangxue , Chen Zhiqin , Huang Hui , Cohen-Or Daniel , Zhang Hao
Published: November 2019

We introduce LOGAN, a deep neural network aimed at learning
generalpurpose
shape transforms from
unpaired
domains. The network is trained on two sets of shapes, e.g., tables and chairs, while there is neither a pairing between shapes from the domains as supervision nor any point-wise correspondence between any shapes. Once trained, LOGAN takes a shape from one domain and transforms it into the other. Our network consists of an autoencoder to encode shapes from the two input domains into a
common latent space
, where the latent codes
concatenate multi-scale
shape features, resulting in an
overcomplete
representation. The translator is based on a generative adversarial network (GAN), operating in the latent space, where an adversarial loss enforces cross-domain translation while a
feature preservation loss
ensures that the right shape features are preserved for a natural shape transform. We conduct ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. We show that LOGAN is able to learn what shape features to preserve during shape translation, either local or non-local, whether content or style, depending solely on the input domains for training.
Cubic stylization
Authors: liu Hsueh-Ti Derek , Jacobson Alec
Published: November 2019

We present a 3D stylization algorithm that can turn an input shape into the style of a cube while maintaining the
content
of the original shape. The key insight is that cubic style sculptures can be captured by the
as-rigid-as-possible
energy with an ℓ
1
-regularization on rotated surface normals. Minimizing this energy naturally leads to a detail-preserving, cubic geometry. Our optimization can be solved efficiently without any mesh surgery. Our method serves as a non-realistic modeling tool where one can incorporate many artistic controls to create stylized geometries.
Neural style-preserving visual dubbing
Authors: Kim Hyeongwoo , Elgharib Mohamed , Zollhöfer Michael , Seidel Hans-Peter , Beeler Thabo , Richardt Christian , Theobalt Christian
Published: November 2019

Dubbing is a technique for translating video content from one language to another. However, state-of-the-art visual dubbing techniques directly copy facial expressions from source to target actors without considering identity-specific idiosyncrasies such as a unique type of smile. We present a style-preserving visual dubbing approach from single video inputs, which maintains the signature style of target actors when modifying facial expressions, including mouth motions, to match foreign languages. At the heart of our approach is the concept of motion style, in particular for facial expressions, i.e., the person-specific expression change that is yet another essential factor beyond visual accuracy in face editing applications. Our method is based on a recurrent generative adversarial network that captures the spatiotemporal co-activation of facial expressions, and enables generating and modifying the facial expressions of the target actor while preserving their style. We train our model with unsynchronized source and target videos in an unsupervised manner using cycle-consistency and mouth expression losses, and synthesize photorealistic video frames using a layered neural face renderer. Our approach generates temporally coherent results, and handles dynamic backgrounds. Our results show that our dubbing approach maintains the idiosyncratic style of the target actor better than previous approaches, even for widely differing source and target actors.
Consistent shepard interpolation for SPH-based fluid animation
Authors: Reinhardt Stefan , Krake Tim , Eberhardt Bernhard , Weiskopf Daniel
Published: November 2019

We present a novel technique to correct errors introduced by the discretization of a fluid body when animating it with smoothed particle hydrodynamics (SPH). Our approach is based on the Shepard correction, which reduces the interpolation errors from irregularly spaced data. With Shepard correction, the smoothing kernel function is normalized using the weighted sum of the kernel function values in the neighborhood. To compute the correction factor, densities of neighboring particles are needed, which themselves are computed with the uncorrected kernel. This results in an inconsistent formulation and an error-prone correction of the kernel. As a consequence, the density computation may be inaccurate, thus the pressure forces are erroneous and may cause instabilities in the simulation process. We present a consistent formulation by using the corrected densities to compute the exact kernel correction factor and, thereby, increase the accuracy of the simulation. Employing our method, a smooth density distribution is achieved, i.e., the noise in the density field is reduced by orders of magnitude. To show that our method is independent of the SPH variant, we evaluate our technique on weakly compressible SPH and on divergence-free SPH. Incorporating the corrected density into the correction process, the problem cannot be stated explicitly anymore. We propose an efficient and easy-to-implement algorithm to solve the implicit problem by applying the power method. Additionally, we demonstrate how our model can be applied to improve the density distribution on rigid bodies when using a well-known rigid-fluid coupling approach.
Neural state machine for character-scene interactions
Authors: Starke Sebastian , Zhang He , Komura Taku , Saito Jun
Published: November 2019

We propose
Neural State Machine
, a novel data-driven framework to guide characters to achieve goal-driven actions with precise scene interactions. Even a seemingly simple task such as sitting on a chair is notoriously hard to model with supervised learning. This difficulty is because such a task involves complex planning with periodic and non-periodic motions reacting to the scene geometry to precisely position and orient the character. Our proposed deep auto-regressive framework enables modeling of multi-modal scene interaction behaviors purely from data. Given high-level instructions such as the goal location and the action to be launched there, our system computes a series of movements and transitions to reach the goal in the desired state. To allow characters to adapt to a wide range of geometry such as different shapes of furniture and obstacles, we incorporate an efficient data augmentation scheme to randomly switch the 3D geometry while maintaining the context of the original motion. To increase the precision to reach the goal during runtime, we introduce a control scheme that combines egocentric inference and goal-centric inference. We demonstrate the versatility of our model with various scene interaction tasks such as sitting on a chair, avoiding obstacles, opening and entering through a door, and picking and carrying objects generated in real-time just from a single model.
Taichi
Authors: Hu Yuanming , Li Tzu-Mao , Anderson Luke , Ragan-Kelley Jonathan , Durand Frédo
Published: November 2019

3D visual computing data are often spatially sparse. To exploit such sparsity, people have developed hierarchical sparse data structures, such as multi-level sparse voxel grids, particles, and 3D hash tables. However, developing and using these high-performance sparse data structures is challenging, due to their intrinsic complexity and overhead. We propose
Taichi
, a new data-oriented programming language for efficiently authoring, accessing, and maintaining such data structures. The language offers a high-level, data structure-agnostic interface for writing computation code. The user independently specifies the data structure. We provide several elementary components with different sparsity properties that can be arbitrarily composed to create a wide range of multi-level sparse data structures. This
decoupling
of data structures from computation makes it easy to experiment with different data structures without changing computation code, and allows users to write computation as if they are working with a dense array. Our compiler then uses the semantics of the data structure and index analysis to automatically optimize for locality, remove redundant operations for coherent accesses, maintain sparsity and memory allocations, and generate efficient parallel and vectorized instructions for CPUs and GPUs.

Our approach yields competitive performance on common computational kernels such as stencil applications, neighbor lookups, and particle scattering. We demonstrate our language by implementing simulation, rendering, and vision tasks including a material point method simulation, finite element analysis, a multigrid Poisson solver for pressure projection, volumetric path tracing, and 3D convolution on sparse grids. Our computation-data structure decoupling allows us to quickly experiment with different data arrangements, and to develop high-performance data structures tailored for specific computational tasks. With
1

1

0
th as many lines of code, we achieve 4.55× higher performance on average, compared to hand-optimized reference implementations.
Extrusion-based ceramics printing with strictly-continuous deposition
Authors: Hergel Jean , Hinz Kevin , Lefebvre Sylvain , Thomaszewski Bernhard
Published: November 2019

We propose a method for integrated tool path planning and support structure generation tailored to the specific constraints of extrusion-based ceramics printing. Existing path generation methods for thermoplastic materials rely on transfer moves to navigate between different print paths in a given layer. However, when printing with clay, these transfer moves can lead to severe artifacts and failure. Our method eliminates transfer moves altogether by generating deposition paths that are continuous within and across layers. Our algorithm is implemented as a sequential top-down pass through the layer stack. In each layer, we detect points that require support, connect support points and model paths, and optimize the shape of the resulting continuous path with respect to length, smoothness, and distance to the model. For each of these subproblems, we propose dedicated solutions that take into account the fabrication constraints imposed by printable clay. We evaluate our method on a set of examples with multiple disconnected components and challenging support requirements. Comparisons to existing path generation methods designed for thermoplastic materials show that our method substantially improves print quality and often makes the difference between success and failure.
Dynamic hair modeling from monocular videos using deep neural networks
Authors: Yang Lingchen , Shi Zefeng , Zheng Youyi , Zhou Kun
Published: November 2019

We introduce a deep learning based framework for modeling dynamic hairs from monocular videos, which could be captured by a commodity video camera or downloaded from Internet. The framework mainly consists of two neural networks, i.e.,
HairSpatNet
for inferring 3D spatial features of hair geometry from 2D image features, and
HairTempNet
for extracting temporal features of hair motions from video frames. The spatial features are represented as 3D occupancy fields depicting the hair volume shapes and 3D orientation fields indicating the hair growing directions. The temporal features are represented as bidirectional 3D warping fields, describing the forward and backward motions of hair strands cross adjacent frames. Both
HairSpatNet
and
HairTempNet
are trained with synthetic hair data. The spatial and temporal features predicted by the networks are subsequently used for growing hair strands with both spatial and temporal consistency. Experiments demonstrate that our method is capable of constructing plausible dynamic hair models that closely resemble the input video, and compares favorably to previous single-view techniques.
Differentiable surface splatting for point-based geometry processing
Authors: Yifan Wang , Serena Felice , Wu Shihao , Öztireli Cengiz , Sorkine-Hornung Olga
Published: November 2019

We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds. Gradients for point locations and normals are carefully designed to handle discontinuities of the rendering function. Regularization terms are introduced to ensure uniform distribution of the points on the underlying surface. We demonstrate applications of DSS to inverse rendering for geometry synthesis and denoising, where large scale topological changes, as well as small scale detail modifications, are accurately and robustly handled without requiring explicit connectivity, outperforming state-of-the-art techniques. The data and code are at https://github.com/yifita/DSS.
Variance-aware multiple importance sampling
Authors: Grittmann Pascal , Georgiev Iliyan , Slusallek Philipp , Křivánek Jaroslav
Published: November 2019

Many existing Monte Carlo methods rely on multiple importance sampling (MIS) to achieve robustness and versatility. Typically, the balance or power heuristics are used, mostly thanks to the seemingly strong guarantees on their variance. We show that these MIS heuristics are oblivious to the effect of certain variance reduction techniques like stratification. This shortcoming is particularly pronounced when unstratified and stratified techniques are combined (e.g., in a bidirectional path tracer). We propose to enhance the balance heuristic by injecting variance estimates of individual techniques, to reduce the variance of the combined estimator in such cases. Our method is simple to implement and introduces little overhead.
A novel framework for inverse procedural texture modeling
Authors: Hu Yiwei , Dorsey Julie , Rushmeier Holly
Published: November 2019

Procedural textures are powerful tools that have been used in graphics for decades. In contrast to the alternative exemplar-based texture synthesis techniques, procedural textures provide user control and fast texture generation with low-storage cost and unlimited texture resolution. However, creating procedural models for complex textures requires a time-consuming process of selecting a combination of procedures and parameters. We present an example-based framework to automatically select procedural models and estimate parameters. In our framework, we consider textures categorized by commonly used high level classes. For each high level class we build a data-driven inverse modeling system based on an extensive collection of real-world textures and procedural texture models in the form of node graphs. We use unsupervised learning on collected real-world images in a texture class to learn sub-classes. We then classify the output of each of the collected procedural models into these sub-classes. For each of the collected models we train a convolutional neural network (CNN) to learn the parameters to produce a specific output texture. To use our framework, a user provides an exemplar texture image within a high level class. The system first classifies the texture into a sub-class, and selects the procedural models that produce output in that sub-class. The pre-trained CNNs of the selected models are used to estimate the parameters of the texture example. With the predicted parameters, the system can generate appropriate procedural textures for the user. The user can easily edit the textures by adjusting the node graph parameters. In a last optional step, style transfer augmentation can be applied to the fitted procedural textures to recover details lost in the procedural modeling process. We demonstrate our framework for four high level classes and show that our inverse modeling system can produce high-quality procedural textures for both structural and non-structural textures.
Write-a-video
Authors: Wang Miao , Yang Guo-Wei , Hu Shi-Min , Yau Shing-Tung , Shamir Ariel
Published: November 2019

We present
Write-A-Video
, a tool for the creation of video montage using mostly text-editing. Given an input themed text and a related video repository either from online websites or personal albums, the tool allows novice users to generate a video montage much more easily than current video editing tools. The resulting video illustrates the given narrative, provides diverse visual content, and follows cinematographic guidelines. The process involves three simple steps: (1) the user provides input, mostly in the form of editing the text, (2) the tool automatically searches for semantically matching candidate shots from the video repository, and (3) an optimization method assembles the video montage. Visual-semantic matching between segmented text and shots is performed by cascaded keyword matching and visual-semantic embedding, that have better accuracy than alternative solutions. The video assembly is formulated as a hybrid optimization problem over a graph of shots, considering temporal constraints, cinematography metrics such as camera movement and tone, and user-specified cinematography idioms. Using our system, users without video editing experience are able to generate appealing videos.
Animating landscape
Authors: Endo Yuki , Kanamori Yoshihiro , Kuriyama Shigeru
Published: November 2019

Automatic generation of a high-quality video from a single image remains a challenging task despite the recent advances in deep generative models. This paper proposes a method that can create a high-resolution, long-term animation using convolutional neural networks (CNNs) from a single landscape image where we mainly focus on skies and waters. Our key observation is that the
motion
(e.g., moving clouds) and
appearance
(e.g., time-varying colors in the sky) in natural scenes have different time scales. We thus learn them separately and predict them with decoupled control while handling future uncertainty in both predictions by introducing latent codes. Unlike previous methods that infer output frames directly, our CNNs predict spatially-smooth intermediate data, i.e., for motion, flow fields for warping, and for appearance, color transfer maps, via self-supervised learning, i.e., without explicitly-provided ground truth. These intermediate data are applied not to each previous output frame, but to the input image only once for each output frame. This design is crucial to alleviate error accumulation in long-term predictions, which is the essential problem in previous recurrent approaches. The output frames can be looped like cinemagraph, and also be controlled directly by specifying latent codes or indirectly via visual annotations. We demonstrate the effectiveness of our method through comparisons with the state-of-the-arts on video prediction as well as appearance manipulation. Resultant videos, codes, and datasets will be available at http://www.cgg.cs.tsukuba.ac.jp/~endo/projects/AnimatingLandscape.
Learned large field-of-view imaging with thin-plate optics
Authors: Peng Yifan , Sun Qilin , Dun Xiong , Wetzstein Gordon , Heidrich Wolfgang , Heide Felix
Published: November 2019

Typical camera optics consist of a system of individual elements that are designed to compensate for the aberrations of a single lens. Recent computational cameras shift some of this correction task from the optics to post-capture processing, reducing the imaging optics to only a few optical elements. However, these systems only achieve reasonable image quality by limiting the field of view (FOV) to a few degrees - effectively ignoring severe off-axis aberrations with blur sizes of multiple hundred pixels.

In this paper, we propose a lens design and learned reconstruction architecture that lift this limitation and provide an order of magnitude increase in field of view using only a single thin-plate lens element. Specifically, we design a lens to produce spatially shift-invariant point spread functions, over the full FOV, that are tailored to the proposed reconstruction architecture. We achieve this with a mixture PSF, consisting of a peak and and a low-pass component, which provides residual contrast instead of a small spot size as in traditional lens designs. To perform the reconstruction, we train a deep network on captured data from a display lab setup, eliminating the need for manual acquisition of training data in the field. We assess the proposed method in simulation and experimentally with a prototype camera system.

We compare our system against existing single-element designs, including an aspherical lens and a pinhole, and we compare against a complex multielement lens, validating high-quality large field-of-view (i.e. 53°) imaging performance using only a single thin-plate element.
3D Ken Burns effect from a single image
Authors: Niklaus Simon , Mai Long , Yang Jimei , Liu Feng
Published: November 2019

The Ken Burns effect allows animating still images with a virtual camera scan and zoom. Adding parallax, which results in the 3D Ken Burns effect, enables significantly more compelling results. Creating such effects manually is time-consuming and demands sophisticated editing skills. Existing automatic methods, however, require multiple input images from varying viewpoints. In this paper, we introduce a framework that synthesizes the 3D Ken Burns effect from a single image, supporting both a fully automatic mode and an interactive mode with the user controlling the camera. Our framework first leverages a depth prediction pipeline, which estimates scene depth that is suitable for view synthesis tasks. To address the limitations of existing depth estimation methods such as geometric distortions, semantic distortions, and inaccurate depth boundaries, we develop a semantic-aware neural network for depth prediction, couple its estimate with a segmentation-based depth adjustment process, and employ a refinement neural network that facilitates accurate depth predictions at object boundaries. According to this depth estimate, our framework then maps the input image to a point cloud and synthesizes the resulting video frames by rendering the point cloud from the corresponding camera positions. To address disocclusions while maintaining geometrically and temporally coherent synthesis results, we utilize context-aware color- and depth-inpainting to fill in the missing information in the extreme views of the camera path, thus extending the scene geometry of the point cloud. Experiments with a wide variety of image content show that our method enables realistic synthesis results. Our study demonstrates that our system allows users to achieve better results while requiring little effort compared to existing solutions for the 3D Ken Burns effect creation.
Learning adaptive hierarchical cuboid abstractions of 3D shape collections
Authors: Sun Chun-Yu , Zou Qian-Fang , Tong Xin , Liu Yang
Published: November 2019

Abstracting man-made 3D objects as assemblies of primitives,
i.e.
, shape abstraction, is an important task in 3D shape understanding and analysis. In this paper, we propose an unsupervised learning method for automatically constructing compact and expressive shape abstractions of 3D objects in a class. The key idea of our approach is an adaptive hierarchical cuboid representation that abstracts a 3D shape with a set of parametric cuboids adaptively selected from a hierarchical and multi-level cuboid representation shared by all objects in the class. The adaptive hierarchical cuboid abstraction offers a compact representation for modeling the variant shape structures and their coherence at different abstraction levels. Based on this representation, we design a convolutional neural network (CNN) for predicting the parameters of each cuboid in the hierarchical cuboid representation and the adaptive selection mask of cuboids for each input 3D shape. For training the CNN from an unlabeled 3D shape collection, we propose a set of novel loss functions to maximize the approximation quality and compactness of the adaptive hierarchical cuboid abstraction and present a progressive training scheme to refine the cuboid parameters and the cuboid selection mask effectively.

We evaluate the effectiveness of our approach on various 3D shape collections and demonstrate its advantages over the existing cuboid abstraction approach. We also illustrate applications of the resulting adaptive cuboid representations in various shape analysis and manipulation tasks.
ScalarFlow
Authors: Eckert Marie-Lena , Um Kiwon , Thuerey Nils
Published: November 2019

In this paper, we present
ScalarFlow
, a first large-scale data set of reconstructions of real-world smoke plumes. In addition, we propose a framework for accurate physics-based reconstructions from a small number of video streams. Central components of our framework are a novel estimation of unseen inflow regions and an efficient optimization scheme constrained by a simulation to capture real-world fluids. Our data set includes a large number of complex natural buoyancy-driven flows. The flows transition to turbulence and contain observable scalar transport processes. As such, the ScalarFlow data set is tailored towards computer graphics, vision, and learning applications. The published data set contains volumetric reconstructions of velocity and density as well as the corresponding input image sequences with calibration data, code, and instructions how to reproduce the commodity hardware capture setup. We further demonstrate one of the many potential applications: a first perceptual evaluation study, which reveals that the complexity of the reconstructed flows would require large simulation resolutions for regular solvers in order to recreate at least parts of the natural complexity contained in the captured data.
SceneGit
Authors: Carra Edoardo , Pellacini Fabio
Published: November 2019

Version control systems are the foundation of collaborative workflows for text documents. For 3D environments though, version control is still an open problem due to the heterogeneous data of 3D scenes and their size. In this paper, we present a practical version control system for 3D scenes comprised of shapes, materials, textures, and animations, combined together in scene graphs. We version objects at their finest granularity, to make repositories smaller and to allow artists to work concurrently on the same object. Since, for some scene data, computing an optimal set of changes between versions is not computationally feasible, version control systems use heuristics. Compared to prior work, we propose heuristics that are efficient, robust, and independent of the application. We test our system on a variety of large scenes edited with different workflows, and show that our approach can handle all cases well while remaining efficient as scene size increases. Compared to prior work, we are significantly faster and more robust. A user study confirms that our system aids collaboration.
DiCE
Authors: Zhong Fangcheng , Koulieris George Alex , Drettakis George , Banks Martin S. , Chambe Mathieu , Durand Frédo , Mantiuk Rafał K.
Published: November 2019

In stereoscopic displays, such as those used in VR/AR headsets, our eyes are presented with two different views. The disparity between the views is typically used to convey depth cues, but it could be also used to enhance image appearance. We devise a novel technique that takes advantage of binocular fusion to boost perceived local contrast and visual quality of images. Since the technique is based on fixed tone curves, it has negligible computational cost and it is well suited for real-time applications, such as VR rendering. To control the trade-off between contrast gain and binocular rivalry, we conduct a series of experiments to explain the factors that dominate rivalry perception in a dichoptic presentation where two images of different contrasts are displayed. With this new finding, we can effectively enhance contrast and control rivalry in mono- and stereoscopic images, and in VR rendering, as confirmed in validation experiments.
Staged metaprogramming for shader system development
Authors: Seitz Kerry A. , Foley Theresa , Porumbescu Serban D. , Owens John D.
Published: November 2019

The shader system for a modern game engine comprises much more than just compilation of source code to executable kernels. Shaders must also be exposed to art tools, interfaced with engine code, and specialized for performance. Engines typically address each of these tasks in an ad hoc fashion, without a unifying abstraction. The alternative of developing a more powerful compiler framework is prohibitive for most engines.

In this paper, we identify
staged metaprogramming
as a unifying abstraction and implementation strategy to develop a powerful shader system with modest effort. By using a multi-stage language to perform metaprogramming at compile time, engine-specific code can consume, analyze, transform, and generate shader code that will execute at runtime. Staged metaprogramming reduces the effort required to implement a shader system that provides earlier error detection, avoids repeat declarations of shader parameters, and explores opportunities to improve performance.

To demonstrate the value of this approach, we design and implement a shader system, called Selos, built using staged metaprogramming. In our system, shader and application code are written in the same language and can share types and functions. We implement a design space exploration framework for Selos that investigates static versus dynamic composition of shader features, exploring the impact of shader specialization in a deferred renderer. Staged metaprogramming allows Selos to provide compelling features with a simple implementation.
Data-driven interior plan generation for residential buildings
Authors: Wu Wenming , Fu Xiao-Ming , Tang Rui , Wang Yuhan , Qi Yu-Hao , Liu Ligang
Published: November 2019

We propose a novel data-driven technique for automatically and efficiently generating floor plans for residential buildings with given boundaries. Central to this method is a two-stage approach that imitates the human design process by locating rooms first and then walls while adapting to the input building boundary. Based on observations of the presence of the living room in almost all floor plans, our designed learning network begins with positioning a living room and continues by iteratively generating other rooms. Then, walls are first determined by an encoder-decoder network, and then they are refined to vector representations using dedicated rules. To effectively train our networks, we construct
RPLAN
- a manually collected large-scale densely annotated dataset of floor plans from real residential buildings. Intensive experiments, including formative user studies and comparisons, are conducted to illustrate the feasibility and efficacy of our proposed approach. By comparing the plausibility of different floor plans, we have observed that our method substantially outperforms existing methods, and in many cases our floor plans are comparable to human-created ones.
Transport-based neural style transfer for smoke simulations
Authors: Kim Byungsoo , Azevedo Vinicius C. , Gross Markus , Solenthaler Barbara
Published: November 2019

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.
Chebyshev nets from commuting PolyVector fields
Authors: Sageman-Furnas Andrew O. , Chern Albert , Ben-Chen Mirela , Vaxman Amir
Published: November 2019

We propose a method for computing global Chebyshev nets on triangular meshes. We formulate the corresponding global parameterization problem in terms of
commuting
PolyVector fields, and design an efficient optimization method to solve it. We compute, for the first time, Chebyshev nets with automatically-placed singularities, and demonstrate the realizability of our approach using real material.