Showing papers in "ACM Transactions on Graphics in 2012"
TL;DR: This article presents an algorithm for learning hatching styles from line drawings, which can be generated in the artist's style by synthesizing hatching strokes according to the target properties.
Abstract: This article presents an algorithm for learning hatching styles from line drawings. An artist draws a single hatching illustration of a 3D object. Her strokes are analyzed to extract the following per-pixel properties: hatching level (hatching, cross-hatching, or no strokes), stroke orientation, spacing, intensity, length, and thickness. A mapping is learned from input geometric, contextual, and shading features of the 3D object to these hatching properties, using classification, regression, and clustering techniques. Then, a new illustration can be generated in the artist's style, as follows. First, given a new view of a 3D object, the learned mapping is applied to synthesize target stroke properties for each pixel. A new illustration is then generated by synthesizing hatching strokes according to the target properties.
696 citations
TL;DR: A new video stabilization technique that uses projective scene reconstruction to treat jittered video sequences and develops a time-view reprojection that accounts for nonstationary points in a principled way to achieve an adequate stabilization when both the camera and the objects are moving.
Abstract: We present a new video stabilization technique that uses projective scene reconstruction to treat jittered video sequences. Unlike methods that recover the full three-dimensional geometry of the scene, this model accounts for simple geometric relations between points and epipolar lines. Using this level of scene understanding, we obtain the physical correctness of 3D stabilization methods yet avoid their lack of robustness and computational costs. Our method consists of tracking feature points in the scene and using them to compute fundamental matrices that model stabilized camera motion. We then project the tracked points onto the novel stabilized frames using epipolar point transfer and synthesize new frames using image-based frame warping. Since this model is only valid for static scenes, we develop a time-view reprojection that accounts for nonstationary points in a principled way. This reprojection is based on modeling the dynamics of smooth inertial object motion in three-dimensional space and allows us to avoid the need to interpolate stabilization for moving objects from their static surrounding. Thus, we achieve an adequate stabilization when both the camera and the objects are moving. We demonstrate the abilities of our approach to stabilize hand-held video shots in various scenarios: scenes with no parallax that challenge 3D approaches, scenes containing nontrivial parallax effects, videos with camera zooming and in-camera stabilization, as well as movies with large moving objects.
163 citations
TL;DR: The bilinear model is applied to natural spatiotemporal phenomena, including face, body, and cloth motion data, and compared in terms of compaction, generalization ability, predictive precision, and efficiency to existing models.
Abstract: A variety of dynamic objects, such as faces, bodies, and cloth, are represented in computer graphics as a collection of moving spatial landmarks. Spatiotemporal data is inherent in a number of graphics applications including animation, simulation, and object and camera tracking. The principal modes of variation in the spatial geometry of objects are typically modeled using dimensionality reduction techniques, while concurrently, trajectory representations like splines and autoregressive models are widely used to exploit the temporal regularity of deformation. In this article, we present the bilinear spatiotemporal basis as a model that simultaneously exploits spatial and temporal regularity while maintaining the ability to generalize well to new sequences. This factorization allows the use of analytical, predefined functions to represent temporal variation (e.g., B-Splines or the Discrete Cosine Transform) resulting in efficient model representation and estimation. The model can be interpreted as representing the data as a linear combination of spatiotemporal sequences consisting of shape modes oscillating over time at key frequencies. We apply the bilinear model to natural spatiotemporal phenomena, including face, body, and cloth motion data, and compare it in terms of compaction, generalization ability, predictive precision, and efficiency to existing models. We demonstrate the application of the model to a number of graphics tasks including labeling, gap-filling, denoising, and motion touch-up.
135 citations
TL;DR: A new forensic technique that focuses on geometric inconsistencies that arise when fake reflections are inserted into a photograph or when a photograph containing reflections is manipulated is described.
Abstract: The advent of sophisticated photo editing software has made it increasingly easier to manipulate digital images. Often visual inspection cannot definitively distinguish the resulting forgeries from authentic photographs. In response, forensic techniques have emerged to detect geometric or statistical inconsistencies that result from specific forms of photo manipulation. In this article we describe a new forensic technique that focuses on geometric inconsistencies that arise when fake reflections are inserted into a photograph or when a photograph containing reflections is manipulated. This analysis employs basic rules of reflective geometry and linear perspective projection, makes minimal assumptions about the scene geometry, and only requires the user to identify corresponding points on an object and its reflection. The analysis is also insensitive to common image editing operations such as resampling, color manipulations, and lossy compression. We demonstrate this technique with both visually plausible forgeries of our own creation and commercially produced forgeries.
134 citations
TL;DR: Random Parameter Filtering (RPF) is proposed, which can produce images in a few minutes that are comparable to those rendered with a thousand times more samples and works for a wide range of Monte Carlo effects, including depth of field, area light sources, motion blur, and path-tracing.
Abstract: Monte Carlo (MC) rendering systems can produce spectacular images but are plagued with noise at low sampling rates. In this work, we observe that this noise occurs in regions of the image where the sample values are a direct function of the random parameters used in the Monte Carlo system. Therefore, we propose a way to identify MC noise by estimating this functional relationship from a small number of input samples. To do this, we treat the rendering system as a black box and calculate the statistical dependency between the outputs and inputs of the system. We then use this information to reduce the importance of the sample values affected by MC noise when applying an image-space, cross-bilateral filter, which removes only the noise caused by the random parameters but preserves important scene detail. The process of using the functional relationships between sample values and the random parameter inputs to filter MC noise is called Random Parameter Filtering (RPF), and we demonstrate that it can produce images in a few minutes that are comparable to those rendered with a thousand times more samples. Furthermore, our algorithm is general because we do not assign any physical meaning to the random parameters, so it works for a wide range of Monte Carlo effects, including depth of field, area light sources, motion blur, and path-tracing. We present results for still images and animated sequences at low sampling rates that have higher quality than those produced with previous approaches.
122 citations
TL;DR: A novel shape completion technique for creating temporally coherent watertight surfaces from real-time captured dynamic performances that does not suffer error accumulation typically introduced by noise, large deformations, and drastic topological changes is presented.
Abstract: We present a novel shape completion technique for creating temporally coherent watertight surfaces from real-time captured dynamic performances. Because of occlusions and low surface albedo, scanned mesh sequences typically exhibit large holes that persist over extended periods of time. Most conventional dynamic shape reconstruction techniques rely on template models or assume slow deformations in the input data. Our framework sidesteps these requirements and directly initializes shape completion with topology derived from the visual hull. To seal the holes with patches that are consistent with the subject's motion, we first minimize surface bending energies in each frame to ensure smooth transitions across hole boundaries. Temporally coherent dynamics of surface patches are obtained by unwarping all frames within a time window using accurate interframe correspondences. Aggregated surface samples are then filtered with a temporal visibility kernel that maximizes the use of nonoccluded surfaces. A key benefit of our shape completion strategy is that it does not rely on long-range correspondences or a template model. Consequently, our method does not suffer error accumulation typically introduced by noise, large deformations, and drastic topological changes. We illustrate the effectiveness of our method on several high-resolution scans of human performances captured with a state-of-the-art multiview 3D acquisition system.
118 citations
TL;DR: A new method is presented which treats both air and liquid as incompressible, more accurately reproducing the reality observed at scales relevant to computer animation, and is shown to reproduce lively two-phase fluid phenomena.
Abstract: Physically-based liquid animations often ignore the influence of air, giving up interesting behavior. We present a new method which treats both air and liquid as incompressible, more accurately reproducing the reality observed at scales relevant to computer animation. The Fluid Implicit Particle (FLIP) method, already shown to effectively simulate incompressible fluids with low numerical dissipation, is extended to two-phase flow by associating a phase bit with each particle. The liquid surface is reproduced at each time step from the particle positions, which are adjusted to prevent mixing near the surface and to allow for accurate surface tension. The liquid surface is adjusted around small-scale features so they are represented in the grid-based pressure projection, while separate, loosely coupled velocity fields reduce unwanted influence between the phases. The resulting scheme is easy to implement, requires little parameter tuning, and is shown to reproduce lively two-phase fluid phenomena.
100 citations
TL;DR: This article considers the problem of animation reconstruction, that is, the reconstruction of shape and motion of a deformable object from dynamic 3D scanner data, without using user-provided template models, and proposes a number of algorithmic building blocks that can handle fast motion, temporally disrupted input, and correctly match objects that disappear for extended time periods in acquisition holes due to occlusion.
Abstract: In this article, we consider the problem of animation reconstruction, that is, the reconstruction of shape and motion of a deformable object from dynamic 3D scanner data, without using user-provided template models. Unlike previous work that addressed this problem, we do not rely on locally convergent optimization but present a system that can handle fast motion, temporally disrupted input, and can correctly match objects that disappear for extended time periods in acquisition holes due to occlusion. Our approach is motivated by cartography: We first estimate a few landmark correspondences, which are extended to a dense matching and then used to reconstruct geometry and motion. We propose a number of algorithmic building blocks: a scheme for tracking landmarks in temporally coherent and incoherent data, an algorithm for robust estimation of dense correspondences under topological noise, and the integration of local matching techniques to refine the result. We describe and evaluate the individual components and propose a complete animation reconstruction pipeline based on these ideas. We evaluate our method on a number of standard benchmark datasets and show that we can obtain correct reconstructions in situations where other techniques fail completely or require additional user guidance such as a template model.
99 citations
TL;DR: Two new parametric models of the Bidirectional Reflectance Distribution Function (BRDF) are presented, oneinspired by the Rayleigh-Rice theory for light scattering from optically smooth surfaces, and one inspired by micro-facet theory, to enable representation of types of surface scattering which previous parametricmodels have had trouble modeling accurately.
Abstract: This article presents two new parametric models of the Bidirectional Reflectance Distribution Function (BRDF), one inspired by the Rayleigh-Rice theory for light scattering from optically smooth surfaces, and one inspired by micro-facet theory. The models represent scattering from a wide range of glossy surface types with high accuracy. In particular, they enable representation of types of surface scattering which previous parametric models have had trouble modeling accurately. In a study of the scattering behavior of measured reflectance data, we investigate what key properties are needed for a model to accurately represent scattering from glossy surfaces. We investigate different parametrizations and how well they match the behavior of measured BRDFs. We also examine the scattering curves which are represented in parametric models by different distribution functions. Based on the insights gained from the study, the new models are designed to provide accurate fittings to the measured data. Importance sampling schemes are developed for the new models, enabling direct use in existing production pipelines. In the resulting renderings we show that the visual quality achieved by the models matches that of the measured data.
94 citations
TL;DR: This article presents a novel spacetime facial animation retargeting method for blendshape face models that provides spacetime editing for the popular blendshape representation of facial models, exhibiting smooth and controlled propagation of user edits across surrounding frames.
Abstract: The goal of a practical facial animation retargeting system is to reproduce the character of a source animation on a target face while providing room for additional creative control by the animator. This article presents a novel spacetime facial animation retargeting method for blendshape face models. Our approach starts from the basic principle that the source and target movements should be similar. By interpreting movement as the derivative of position with time, and adding suitable boundary conditions, we formulate the retargeting problem as a Poisson equation. Specified (e.g., neutral) expressions at the beginning and end of the animation as well as any user-specified constraints in the middle of the animation serve as boundary conditions. In addition, a model-specific prior is constructed to represent the plausible expression space of the target face during retargeting. A Bayesian formulation is then employed to produce target animation that is consistent with the source movements while satisfying the prior constraints. Since the preservation of temporal derivatives is the primary goal of the optimization, the retargeted motion preserves the rhythm and character of the source movement and is free of temporal jitter. More importantly, our approach provides spacetime editing for the popular blendshape representation of facial models, exhibiting smooth and controlled propagation of user edits across surrounding frames.
94 citations
TL;DR: A novel method for high-performance GPU-based rendering of Catmull-Clark subdivision surfaces which computes the true limit surface up to machine precision, and is capable of rendering surfaces that conform to the full RenderMan specification for Catm Mull-Clark surfaces.
Abstract: We present a novel method for high-performance GPU-based rendering of Catmull-Clark subdivision surfaces. Unlike previous methods, our algorithm computes the true limit surface up to machine precision, and is capable of rendering surfaces that conform to the full RenderMan specification for Catmull-Clark surfaces. Specifically, our algorithm can accommodate base meshes consisting of arbitrary valence vertices and faces, and the surface can contain any number and arrangement of semisharp creases and hierarchically defined detail. We also present a variant of the algorithm which guarantees watertight positions and normals, meaning that even displaced surfaces can be rendered in a crack-free manner. Finally, we describe a view-dependent level-of-detail scheme which adapts to both the depth of subdivision and the patch tessellation density. Though considerably more general, the performance of our algorithm is comparable to the best approximating method, and is considerably faster than Stam's exact method.
TL;DR: This work builds the first system that achieves high-resolution and high-quality appearance edits using multiple superimposed projectors on complex nonplanar colored objects and demonstrates several appearance edits including specular lighting, subsurface scattering, inter-reflections, and color, texture, and geometry changes on objects with different shapes and colors.
Abstract: We present a system that superimposes multiple projections onto an object of arbitrary shape and color to produce high-resolution appearance changes. Our system produces appearances at an improved resolution compared to prior works and can change appearances at near interactive rates. Three main components are central to our system. First, the problem of computing compensation images is formulated as a constrained optimization which yields high-resolution appearances. Second, decomposition of the target appearance into base and scale images enables fast swapping of appearances on the object by requiring the constrained optimization to be computed only once per object. Finally, to make high-quality appearance edits practical, an elliptical Gaussian is used to model projector pixels and their interaction between projectors. To the best of our knowledge, we build the first system that achieves high-resolution and high-quality appearance edits using multiple superimposed projectors on complex nonplanar colored objects. We demonstrate several appearance edits including specular lighting, subsurface scattering, inter-reflections, and color, texture, and geometry changes on objects with different shapes and colors.
TL;DR: A variational mesh decomposition algorithm that can efficiently partition a mesh into a prescribed number of segments is presented that outperforms competitive segmentation methods when evaluated on the Princeton Segmentation Benchmark.
Abstract: The problem of decomposing a 3D mesh into meaningful segments (or parts) is of great practical importance in computer graphics. This article presents a variational mesh decomposition algorithm that can efficiently partition a mesh into a prescribed number of segments. The algorithm extends the Mumford-Shah model to 3D meshes that contains a data term measuring the variation within a segment using eigenvectors of a dual Laplacian matrix whose weights are related to the dihedral angle between adjacent triangles and a regularization term measuring the length of the boundary between segments. Such a formulation simultaneously handles segmentation and boundary smoothing, which are usually two separate processes in most previous work. The efficiency is achieved by solving the Mumford-Shah model through a saddle-point problem that is solved by a fast primal-dual method. A preprocess step is also proposed to determine the number of segments that the mesh should be decomposed into. By incorporating this preprocessing step, the proposed algorithm can automatically segment a mesh into meaningful parts. Furthermore, user interaction is allowed by incorporating the user's inputs into the variational model to reflect the user's special intention. Experimental results show that the proposed algorithm outperforms competitive segmentation methods when evaluated on the Princeton Segmentation Benchmark.
TL;DR: A physically-based model is derived that matches Lorenz-Mie theory for spherical particles, but it also enables the accurate simulation of nonspherical particles, which can simulate many different rainbow phenomena including double rainbows and supernumerary bows.
Abstract: In this article, we derive a physically-based model for simulating rainbows. Previous techniques for simulating rainbows have used either geometric optics (ray tracing) or Lorenz-Mie theory. Lorenz-Mie theory is by far the most accurate technique as it takes into account optical effects such as dispersion, polarization, interference, and diffraction. These effects are critical for simulating rainbows accurately. However, as Lorenz-Mie theory is restricted to scattering by spherical particles, it cannot be applied to real raindrops which are nonspherical, especially for larger raindrops. We present the first comprehensive technique for simulating the interaction of a wavefront of light with a physically-based water drop shape. Our technique is based on ray tracing extended to account for dispersion, polarization, interference, and diffraction. Our model matches Lorenz-Mie theory for spherical particles, but it also enables the accurate simulation of nonspherical particles. It can simulate many different rainbow phenomena including double rainbows and supernumerary bows. We show how the nonspherical raindrops influence the shape of the rainbows, and we provide a simulation of the rare twinned rainbow, which is believed to be caused by nonspherical water drops.
TL;DR: This work presents an algorithm for the simulation of incompressible fluid phenomena that is computationally efficient and leads to visually convincing simulations with far fewer degrees of freedom than existing approaches, and chooses Laplacian eigenfunctions for this basis.
Abstract: We present an algorithm for the simulation of incompressible fluid phenomena that is computationally efficient and leads to visually convincing simulations with far fewer degrees of freedom than existing approaches. Rather than using an Eulerian grid or Lagrangian elements, we represent vorticity and velocity using a basis of global functions defined over the entire simulation domain. We show that choosing Laplacian eigenfunctions for this basis provides benefits, including correspondence with spatial scales of vorticity and precise energy control at each scale. We perform Galerkin projection of the Navier-Stokes equations to derive a time evolution equation in the space of basis coefficients. Our method admits closed-form solutions on simple domains but can also be implemented efficiently on arbitrary meshes.
TL;DR: W warp-canceling corotation is presented, a nonlinear finite element formulation for elastodynamic simulation that achieves fast performance by making only partial or delayed changes to the simulation's linearized system matrices.
Abstract: We present warp-canceling corotation, a nonlinear finite element formulation for elastodynamic simulation that achieves fast performance by making only partial or delayed changes to the simulation's linearized system matrices. Coupled with an algorithm for incremental updates to a sparse Cholesky factorization, the method realizes the stability and scalability of a sparse direct method without the need for expensive refactorization at each time step. This finite element formulation combines the widely used corotational method with stiffness warping so that changes in the per-element rotations are initially approximated by inexpensive per-node rotations. When the errors of this approximation grow too large, the per-element rotations are selectively corrected by updating parts of the matrix chosen according to locally measured errors. These changes to the system matrix are propagated to its Cholesky factor by incremental updates that are much faster than refactoring the matrix from scratch. A nested dissection ordering of the system matrix gives rise to a hierarchical factorization in which changes to the system matrix cause limited, well-structured changes to the Cholesky factor. We show examples of simulations that demonstrate that the proposed formulation produces results that are visually comparable to those produced by a standard corotational formulation. Because our method requires computing only partial updates of the Cholesky factor, it is substantially faster than full refactorization and outperforms widely used iterative methods such as preconditioned conjugate gradients. Our method supports a controlled trade-off between accuracy and speed, and unlike most iterative methods its performance does not slow for stiffer materials but rather it actually improves.
TL;DR: A new scattering model for woven cloth is introduced that describes the reflectance and the texture based on an analysis of specular reflection from the fibers, which can handle a wide range of fabrics using a small set of physically meaningful parameters.
Abstract: The appearance of a particular fabric is produced by variations in both large-scale reflectance and small-scale texture as the viewing and illumination angles change across the surface. This article presents a study of the reflectance and texture of woven cloth that aims to identify and model important optical features of cloth appearance. New measurements are reported for a range of fabrics including natural and synthetic fibers as well as staple and filament yarns. A new scattering model for woven cloth is introduced that describes the reflectance and the texture based on an analysis of specular reflection from the fibers. Unlike data-based models, our procedural model doesn't require image data. It can handle a wide range of fabrics using a small set of physically meaningful parameters that describe the characteristics of the fibers, the geometry of the yarns, and the pattern of the weave. The model is validated against the measurements and evaluated by comparisons to high-resolution video of the real fabrics and to BTF models of two of the fabrics.
TL;DR: In the DSC method, the interface is represented explicitly as a piecewise linear curve which is a part of a discretization of the space, such that the interface can be retrieved as a set of faces separating triangles/tetrahedra marked as inside from the ones marked as outside.
Abstract: We present a novel, topology-adaptive method for deformable interface tracking, called the Deformable Simplicial Complex (DSC). In the DSC method, the interface is represented explicitly as a piecewise linear curve (in 2D) or surface (in 3D) which is a part of a discretization (triangulation/tetrahedralization) of the space, such that the interface can be retrieved as a set of faces separating triangles/tetrahedra marked as inside from the ones marked as outside (so it is also given implicitly). This representation allows robust topological adaptivity and, thanks to the explicit representation of the interface, it suffers only slightly from numerical diffusion. Furthermore, the use of an unstructured grid yields robust adaptive resolution. Also, topology control is simple in this setting. We present the strengths of the method in several examples: simple geometric flows, fluid simulation, point cloud reconstruction, and cut locus construction.
TL;DR: The theory in this article provides guidelines for selecting visibility sampling strategies, which can reduce the number of shadow samples by 20--40%, with simple modifications to existing rendering code.
Abstract: Soft shadows from area lights are one of the most crucial effects in high-quality and production rendering, but Monte-Carlo sampling of visibility is often the main source of noise in rendered images. Indeed, it is common to use deterministic uniform sampling for the smoother shading effects in direct lighting, so that all of the Monte Carlo noise arises from visibility sampling alone. In this article, we analyze theoretically and empirically, using both statistical and Fourier methods, the effectiveness of different nonadaptive Monte Carlo sampling patterns for rendering soft shadows.We start with a single image scanline and a linear light source, and gradually consider more complex visibility functions at a pixel. We show analytically that the lowest expected variance is in fact achieved by uniform sampling (albeit at the cost of visual banding artifacts). Surprisingly, we show that for two or more discontinuities in the visibility function, a comparable error to uniform sampling is obtained by “uniform jitter” sampling, where a constant jitter is applied to all samples in a uniform pattern (as opposed to jittering each stratum as in standard stratified sampling). The variance can be reduced by up to a factor of two, compared to stratified or quasi-Monte Carlo techniques, without the banding in uniform sampling.We augment our statistical analysis with a novel 2D Fourier analysis across the pixel-light space. This allows us to characterize the banding frequencies in uniform sampling, and gives insights into the behavior of uniform jitter and stratified sampling. We next extend these results to planar area light sources. We show that the best sampling method can vary, depending on the type of light source (circular, Gaussian, or square/rectangular). The correlation of adjacent “light scanlines” in square light sources can reduce the effectiveness of uniform jitter sampling, while the smoother shape of circular and Gaussian-modulated sources preserves its benefits—these findings are also exposed through our frequency analysis. In practical terms, the theory in this article provides guidelines for selecting visibility sampling strategies, which can reduce the number of shadow samples by 20--40p, with simple modifications to existing rendering code.
TL;DR: A novel method of simulating wave effects in graphics using ray-based renderers with a new function: the Wave BSDF (Bidirectional Scattering Distribution Function), which circumvent the need to explicitly keep track of the phase of the wave by using BSDFs that include positive as well as negative coefficients.
Abstract: We present a novel method of simulating wave effects in graphics using ray-based renderers with a new function: the Wave BSDF (Bidirectional Scattering Distribution Function). Reflections from neighboring surface patches represented by local BSDFs are mutually independent. However, in many surfaces with wavelength-scale microstructures, interference and diffraction requires a joint analysis of reflected wavefronts from neighboring patches. We demonstrate a simple method to compute the BSDF for the entire microstructure, which can be used independently for each patch. This allows us to use traditional ray-based rendering pipelines to synthesize wave effects. We exploit the Wigner Distribution Function (WDF) to create transmissive, reflective, and emissive BSDFs for various diffraction phenomena in a physically accurate way. In contrast to previous methods for computing interference, we circumvent the need to explicitly keep track of the phase of the wave by using BSDFs that include positive as well as negative coefficients. We describe and compare the theory in relation to well-understood concepts in rendering and demonstrate a straightforward implementation. In conjunction with standard raytracers, such as PBRT, we demonstrate wave effects for a range of scenarios such as multibounce diffraction materials, holograms, and reflection of high-frequency surfaces.
TL;DR: This work advocates a tighter integration of human computation into online, interactive algorithms and presents three specific examples for the design of micro perceptual human computation algorithms to extract depth layers and image normals from a single photograph, and to augment an image with high-level semantic information such as symmetry.
Abstract: Human Computation (HC) utilizes humans to solve problems or carry out tasks that are hard for pure computational algorithms Many graphics and vision problems have such tasks Previous HC approaches mainly focus on generating data in batch, to gather benchmarks, or perform surveys demanding nontrivial interactions We advocate a tighter integration of human computation into online, interactive algorithms We aim to distill the differences between humans and computers and maximize the advantages of both in one algorithm Our key idea is to decompose such a problem into a massive number of very simple, carefully designed, human micro-tasks that are based on perception, and whose answers can be combined algorithmically to solve the original problem Our approach is inspired by previous work on micro-tasks and perception experiments We present three specific examples for the design of micro perceptual human computation algorithms to extract depth layers and image normals from a single photograph, and to augment an image with high-level semantic information such as symmetry
TL;DR: This work presents an approach to printing the reflectance functions of an object or scene so that its appearance is modified correctly as a function of the lighting conditions when viewing the print.
Abstract: The reflectance function of a scene point captures the appearance of that point as a function of lighting direction. We present an approach to printing the reflectance functions of an object or scene so that its appearance is modified correctly as a function of the lighting conditions when viewing the print. For example, such a “photograph” of a statue printed with our approach appears to cast shadows to the right when the “photograph” is illuminated from the left. Viewing the same print with lighting from the right will cause the statue's shadows to be cast to the left. Beyond shadows, all effects due to the lighting variation, such as Lambertian shading, specularity, and inter-reflection can be reproduced. We achieve this ability by geometrically and photometrically controlling specular highlights on the surface of the print. For a particular viewpoint, arbitrary reflectance functions can be built up at each pixel by controlling only the specular highlights and avoiding significant diffuse reflections. Our initial binary prototype uses halftoning to approximate continuous grayscale reflectance functions.
TL;DR: A method that makes use of the retinal integration time in the human visual system for increasing the resolution of displays, and draws a formal connection between the display and super-resolution techniques and finds that both methods share the same limitation.
Abstract: We present a method that makes use of the retinal integration time in the human visual system for increasing the resolution of displays. Given an input image with a resolution higher than the display resolution, we compute several images that match the display's native resolution. We then render these low-resolution images in a sequence that repeats itself on a high refresh-rate display. The period of the sequence falls below the retinal integration time and therefore the eye integrates the images temporally and perceives them as one image. In order to achieve resolution enhancement we apply small-amplitude vibrations to the display panel and synchronize them with the screen refresh cycles. We derive the perceived image model and use it to compute the low-resolution images that are optimized to enhance the apparent resolution of the perceived image. This approach achieves resolution enhancement without having to move the displayed content across the screen and hence offers a more practical solution than existing approaches. Moreover, we use our model to establish limitations on the amount of resolution enhancement achievable by such display systems. In this analysis we draw a formal connection between our display and super-resolution techniques and find that both methods share the same limitation, yet this limitation stems from different sources. Finally, we describe in detail a simple physical realization of our display system and demonstrate its ability to match most of the spectrum displayable on a screen with twice the resolution.
TL;DR: A real-time algorithm that finds the Penetration Depth between general polygonal models based on iterative and local optimization techniques and solves the Linear Complementarity Problem (LCP) using a type of Gauss-Seidel iterative algorithm.
Abstract: We present a real-time algorithm that finds the Penetration Depth (PD) between general polygonal models based on iterative and local optimization techniques. Given an in-collision configuration of an object in configuration space, we find an initial collision-free configuration using several methods such as centroid difference, maximally clear configuration, motion coherence, random configuration, and sampling-based search. We project this configuration on to a local contact space using a variant of continuous collision detection algorithm and construct a linear convex cone around the projected configuration. We then formulate a new projection of the in-collision configuration onto the convex cone as a Linear Complementarity Problem (LCP), which we solve using a type of Gauss-Seidel iterative algorithm. We repeat this procedure until a locally optimal PD is obtained. Our algorithm can process complicated models consisting of tens of thousands triangles at interactive rates.
TL;DR: An interactive sound propagation algorithm that can compute high orders of specular and diffuse reflections as well as edge diffractions in response to moving sound sources and a moving listener is presented.
Abstract: We present an interactive sound propagation algorithm that can compute high orders of specular and diffuse reflections as well as edge diffractions in response to moving sound sources and a moving listener. Our formulation is based on a precomputed acoustic transfer operator, which we compactly represent using the Karhunen-Loeve transform. At runtime, we use a two-pass approach that combines acoustic radiance transfer with interactive ray tracing to compute early reflections as well as higher-order reflections and late reverberation. The overall approach allows accuracy to be traded off for improved performance at runtime, and has a low memory overhead. We demonstrate the performance of our algorithm on different scenarios, including an integration of our algorithm with Valve's Source game engine.
TL;DR: This work identifies 3D proxies to connect hand-drawn animation and 3D computer animation and demonstrates how this approach enables one medium to take advantage of techniques developed for the other; for example, 3D physical simulation is used to create clothes for a hand-animated character, and a traditionally trained animator is able to influence the performance of a 3D character while drawing with paper and pencil.
Abstract: Drawing shapes by hand and manipulating computer-generated objects are the two dominant forms of animation. Though each medium has its own advantages, the techniques developed for one medium are not easily leveraged in the other medium because hand animation is two-dimensional, and inferring the third dimension is mathematically ambiguous. A second challenge is that the character is a consistent three-dimensional (3D) object in computer animation while hand animators introduce geometric inconsistencies in the two-dimensional (2D) shapes to better convey a character's emotional state and personality. In this work, we identify 3D proxies to connect hand-drawn animation and 3D computer animation. We present an integrated approach to generate three levels of 3D proxies: single-points, polygonal shapes, and a full joint hierarchy. We demonstrate how this approach enables one medium to take advantage of techniques developed for the other; for example, 3D physical simulation is used to create clothes for a hand-animated character, and a traditionally trained animator is able to influence the performance of a 3D character while drawing with paper and pencil.
TL;DR: The theory of 2D light transport is used to implement algorithms such as Monte Carlo raytracing, path tracing, irradiance caching, and photon mapping in 2D, and it is demonstrated that these algorithms can be analyzed more easily in this domain while still providing insights for 3D rendering.
Abstract: We investigate global illumination in 2D and show how this simplified problem domain leads to practical insights for 3D rendering.We first derive a full theory of 2D light transport by introducing 2D analogs to radiometric quantities such as flux and radiance, and deriving a 2D rendering equation. We use our theory to show how to implement algorithms such as Monte Carlo raytracing, path tracing, irradiance caching, and photon mapping in 2D, and demonstrate that these algorithms can be analyzed more easily in this domain while still providing insights for 3D rendering.We apply our theory to develop several practical improvements to the irradiance caching algorithm. We perform a full second-order analysis of diffuse indirect illumination, first in 2D, and then in 3D by deriving the irradiance Hessian, and show how this leads to increased accuracy and performance for irradiance caching. We propose second-order Taylor expansion from cache points, which results in more accurate irradiance reconstruction. We also introduce a novel error metric to guide cache point placement by analyzing the error produced by irradiance caching. Our error metric naturally supports anisotropic reconstruction and, in our preliminary study, resulted in an order of magnitude less error than the “split-sphere” heuristic when using the same number of cache points.
TL;DR: A simple formula for 4-point planar warping that produces provably good 2D deformations is introduced and it is demonstrated that, practically, these local operators can be used to create compound deformations with fewer control points and smaller worst-case distortions in comparisons to the state-of-the-art.
Abstract: We introduce a simple formula for 4-point planar warping that produces provably good 2D deformations. In contrast to previous work, the new deformations minimize the maximum conformal distortion and spread the distortion equally across the domain. We derive closed-form formulas for computing the 4-point interpolant and analyze its properties. We further explore applications to 2D shape deformations by building local deformation operators that use thin-plate splines to further deform the 4-point interpolant to satisfy certain boundary conditions. Although this modification no longer has any theoretical guarantees, we demonstrate that, practically, these local operators can be used to create compound deformations with fewer control points and smaller worst-case distortions in comparisons to the state-of-the-art.
TL;DR: This article presents a framework for symmetry-guided texture synthesis and processing by providing methods to transfer symmetry representations from one texture to another, process the symmetries of a texture, and optimize textures with respect to properties of their symmetry representations.
Abstract: This article presents a framework for symmetry-guided texture synthesis and processing. It is motivated by the long-standing problem of how to optimize, transfer, and control the spatial patterns in textures. The key idea is that symmetry representations that measure autocorrelations with respect to all transformations of a group are a natural way to describe spatial patterns in many real-world textures. To leverage this idea, we provide methods to transfer symmetry representations from one texture to another, process the symmetries of a texture, and optimize textures with respect to properties of their symmetry representations. These methods are automatic and robust, as they don't require explicit detection of discrete symmetries. Applications are investigated for optimizing, processing, and transferring symmetries and textures.
TL;DR: Experimental results demonstrate that K-CTA can accurately approximate spatially varying visual datasets, such as bidirectional texture functions, view-dependent occlusiontexture functions, and biscale radiance transfer functions for efficient rendering in real-time applications.
Abstract: With the increasing demands for photo-realistic image synthesis in real time, we propose a sparse multilinear model, which is named K-Clustered Tensor Approximation (K-CTA), to efficiently analyze and approximate large-scale multidimensional visual datasets, so that both storage space and rendering time are substantially reduced. K-CTA not only extends previous work on Clustered Tensor Approximation (CTA) to exploit inter-cluster coherence, but also allows a compact and sparse representation for high-dimensional datasets with just a few low-order factors and reduced multidimensional cluster core tensors. Thus, K-CTA can be regarded as a sparse extension of CTA and a multilinear generalization of sparse representation. Experimental results demonstrate that K-CTA can accurately approximate spatially varying visual datasets, such as bidirectional texture functions, view-dependent occlusion texture functions, and biscale radiance transfer functions for efficient rendering in real-time applications.