SIGGRAPH 2018 论文列表

We present the first computational tool to help ordinary users create transforming pop-up books. In each transforming pop-up, when the user pulls a tab, an initial flat two-dimensional (2D) pattern, i.e., a 2D shape with a superimposed picture, such as an airplane, turns into a new 2D pattern, such as a robot. Given the two 2D patterns, our approach automatically computes a 3D pop-up mechanism that transforms one pattern into the other; it also outputs a design blueprint, allowing the user to easily make the final model. We also present a theoretical analysis of basic transformation mechanisms; combining these basic mechanisms allows more flexibility of final designs. Using our approach, inexperienced users can create models in a short time; previously, even experienced artists often took weeks to manually create them. We demonstrate our method on a variety of real-world examples.

Additive manufacturing has recently seen drastic improvements in resolution, making it now possible to fabricate features at scales of hundreds or even dozens of nanometers, which previously required very expensive lithographic methods. As a result, additive manufacturing now seems poised for optical applications, including those relevant to computer graphics, such as material design, as well as display and imaging applications. In this work, we explore the use of additive manufacturing for generating structural colors, where the structures are designed using a fabrication-aware optimization process. This requires a combination of full-wave simulation, a feasible parameterization of the design space, and a tailored optimization procedure. Many of these components should be re-usable for the design of other optical structures at this scale. We show initial results of material samples fabricated based on our designs. While these suffer from the prototype character of state-of-the-art fabrication hardware, we believe they clearly demonstrate the potential of additive nanofabrication for structural colors and other graphics applications.

Packed atlases, consisting of 2D parameterized charts, are ubiquitously used to store surface signals such as texture or normals. Tight packing is similarly used to arrange and cut-out 2D panels for fabrication from sheet materials. Packing efficiency, or the ratio between the areas of the packed atlas and its bounding box, significantly impacts downstream applications. We propose Box Cutter, a new method for optimizing packing efficiency suitable for both settings. Our algorithm improves packing efficiency without changing distortion by strategically cutting and repacking the atlas charts or panels. It preserves the local mapping between the 3D surface and the atlas charts and retains global mapping continuity across the newly formed cuts. We balance packing efficiency improvement against increase in chart boundary length and enable users to directly control the acceptable amount of boundary elongation. While the problem we address is NP-hard, we provide an effective practical solution by iteratively detecting large rectangular empty spaces, or void boxes, in the current atlas packing and eliminating them by first refining the atlas using strategically placed axis-aligned cuts and then repacking the refined charts. We repeat this process until no further improvement is possible, or until the desired balance between packing improvement and boundary elongation is achieved. Packed chart atlases are only useful for the applications we address if their charts are overlap-free; yet many popular parameterization methods, used as-is, produce atlases with global overlaps. Our pre-processing step eliminates all input overlaps while explicitly minimizing the boundary length of the resulting overlap-free charts. We demonstrate our combined strategy on a large range of input atlases produced by diverse parameterization methods, as well as on multiple sets of 2D fabrication panels. Our framework dramatically improves the output packing efficiency on all inputs; for instance with boundary length increase capped at 50% we improve packing efficiency by 68% on average.

A conformal flattening maps a curved surface to the plane without distorting angles—such maps have become a fundamental building block for problems in geometry processing, numerical simulation, and computational design. Yet existing methods provide little direct control over the shape of the flattened domain, or else demand expensive nonlinear optimization. Boundary first flattening (BFF) is a linear method for conformal parameterization that is faster than traditional linear methods, yet provides control and quality comparable to sophisticated nonlinear schemes. The key insight is that the boundary data for many conformal mapping problems can be efficiently constructed via the Cherrier formula together with a pair of Poincaré-Steklov operators; once the boundary is known, the map can be easily extended over the rest of the domain. Since computation demands only a single factorization of the real Laplace matrix, the amortized cost is about 50× less than any previously published technique for boundary-controlled conformal flattening. As a result, BFF opens the door to real-time editing or fast optimization of high-resolution maps, with direct control over boundary length or angle. We show how this method can be used to construct maps with sharp corners, cone singularities, minimal area distortion, and uniformization over the unit disk; we also demonstrate for the first time how a surface can be conformally flattened directly onto any given target shape.

Optimizing distortion energies over a mesh, in two or three dimensions, is a common and critical problem in physical simulation and geometry processing. We present three new improvements to the state of the art: a barrier-aware line-search filter that cures blocked descent steps due to element barrier terms and so enables rapid progress; an energy proxy model that adaptively blends the Sobolev (inverse-Laplacian-processed) gradient and L-BFGS descent to gain the advantages of both, while avoiding L-BFGS's current limitations in distortion optimization tasks; and a characteristic gradient norm providing a robust and largely mesh- and energy-independent convergence criterion that avoids wrongful termination when algorithms temporarily slow their progress. Together these improvements form the basis for Blended Cured Quasi-Newton (BCQN), a new distortion optimization algorithm. Over a wide range of problems over all scales we show that BCQN is generally the fastest and most robust method available, making some previously intractable problems practical while offering up to an order of magnitude improvement in others.

Direct illumination calculation is an important component of any physically-based Tenderer with a substantial impact on the overall performance. We present a novel adaptive solution for unbiased Monte Carlo direct illumination sampling, based on online learning of the light selection probability distributions. Our main contribution is a formulation of the learning process as Bayesian regression, based on a new, specifically designed statistical model of direct illumination. The net result is a set of regularization strategies to prevent over-fitting and ensure robustness even in early stages of calculation, when the observed information is sparse. The regression model captures spatial variation of illumination, which enables aggregating statistics over relatively large scene regions and, in turn, ensures a fast learning rate. We make the method scalable by adopting a light clustering strategy from the Lightcuts method, and further reduce variance through the use of control variates. As a main design feature, the resulting algorithm is virtually free of any preprocessing, which enables its use for interactive progressive rendering, while the online learning still enables super-linear convergence.

Distributing a simulation across many machines can drastically speed up computations and increase detail. The computing cloud provides tremendous computing resources, but weak service guarantees force programs to manage significant system complexity: nodes, networks, and storage occasionally perform poorly or fail. We describe Nimbus, a system that automatically distributes grid-based and hybrid simulations across cloud computing nodes. The main simulation loop is sequential code and launches distributed computations across many cores. The simulation on each core runs as if it is stand-alone: Nimbus automatically stitches these simulations into a single, larger one. To do this efficiently, Nimbus introduces a four-layer data model that translates between the contiguous, geometric objects used by simulation libraries and the replicated, fine-grain objects managed by its underlying cloud computing runtime. Using PhysBAM particle-level set fluid simulations, we demonstrate that Nimbus can run higher detail simulations faster, distribute simulations on up to 512 cores, and run enormous simulations (10243 cells). Nimbus automatically manages these distributed simulations, balancing load across nodes and recovering from failures. Implementations of PhysBAM water and smoke simulations as well as an open source heat-diffusion simulation show that Nimbus is general and can support complex simulations. Nimbus can be downloaded from https://nimbus.stanford.edu.

We present the first computational approach that can transform three-dimensional (3D) meshes, created by traditional modeling programs, directly into instructions for a computer-controlled knitting machine. Knitting machines are able to robustly and repeatably form knitted 3D surfaces from yarn but have many constraints on what they can fabricate. Given user-defined starting and ending points on an input mesh, our system incrementally builds a helix-free, quad-dominant mesh with uniform edge lengths, runs a tracing procedure over this mesh to generate a knitting path, and schedules the knitting instructions for this path in a way that is compatible with machine constraints. We demonstrate our approach on a wide range of 3D meshes.

Digital sculpting is a popular means to create 3D models but remains a challenging task. We propose a 3D sculpting system that assists users, especially novices, in freely creating models with reduced input labor and enhanced output quality. With an interactive sculpting interface, our system silently records and analyzes users' workflows including brush strokes and camera movements, and predicts what they might do in the future. Users can accept, partially accept, or ignore the suggestions and thus retain full control and individual style. They can also explicitly select and clone past workflows over output model regions. Our key idea is to consider how a model is authored via dynamic workflows in addition to what is shaped in static geometry. This allows our method for more accurate analysis of user intentions and more general synthesis of shape structures than prior workflow or geometry methods, such as large overlapping deformations. We evaluate our method via user feedbacks and authored models.

Endowing 3D objects with realistic surface appearance is a challenging and time-demanding task, as real-world surfaces typically exhibit a plethora of spatially variant geometric and photometric detail. Not surprisingly, computer artists commonly use images of real-world objects as an inspiration and a reference for their digital creations. However, despite two decades of research on image-based modeling, there are still no tools available for automatically extracting the detailed appearance (microgeometry and texture) of a 3D surface from a single image. In this article, we present a novel user-assisted approach for quickly and easily extracting a nonparametric appearance model from a single photograph of a reference object. The extraction process requires a user-provided proxy, whose geometry roughly approximates that of the object in the image. Since the proxy is just a rough approximation, it is necessary to align and deform it so as to match the reference object. The main contribution of this work is a novel technique to perform such an alignment, which enables accurate joint recovery of geometric detail and reflectance. The correlations between the recovered geometry at various scales and the spatially varying reflectance constitute a nonparametric appearance model. Once extracted, the appearance model may then be applied to various 3D shapes, whose large-scale geometry may differ considerably from that of the original reference object. Thus, our approach makes it possible to construct an appearance library, allowing users to easily enrich detail-less 3D shapes with realistic geometric detail and surface texture.

In this paper, we present a mixed explicit and semi-implicit Material Point Method for simulating particle-laden flows. We develop a Multigrid Preconditioned fluid solver for the Locally Averaged Navier Stokes equation. This is discretized purely on a semi-staggered standard MPM grid. Sedimentation is modeled with the Drucker-Prager elastoplasticity flow rule, enhanced by a novel particle density estimation method for converting particles between representations of either continuum or discrete points. Fluid and sediment are two-way coupled through a momentum exchange force that can be easily resolved with two MPM background grids. We present various results to demonstrate the efficacy of our method.

Many computer graphics problems require computing geometric shapes subject to certain constraints. This often results in non-linear and non-convex optimization problems with globally coupled variables, which pose great challenge for interactive applications. Local-global solvers developed in recent years can quickly compute an approximate solution to such problems, making them an attractive choice for applications that prioritize efficiency over accuracy. However, these solvers suffer from lower convergence rate, and may take a long time to compute an accurate result. In this paper, we propose a simple and effective technique to accelerate the convergence of such solvers. By treating each local-global step as a fixed-point iteration, we apply Anderson acceleration, a well-established technique for fixed-point solvers, to speed up the convergence of a local-global solver. To address the stability issue of classical Anderson acceleration, we propose a simple strategy to guarantee the decrease of target energy and ensure its global convergence. In addition, we analyze the connection between Anderson acceleration and quasi-Newton methods, and show that the canonical choice of its mixing parameter is suitable for accelerating local-global solvers. Moreover, our technique is effective beyond classical local-global solvers, and can be applied to iterative methods with a common structure. We evaluate the performance of our technique on a variety of geometry optimization and physics simulation problems. Our approach significantly reduces the number of iterations required to compute an accurate result, with only a slight increase of computational cost per iteration. Its simplicity and effectiveness makes it a promising tool for accelerating existing algorithms as well as designing efficient new algorithms.

Spherical Harmonic (SH) lighting is widely used for real-time rendering within Precomputed Radiance Transfer (PRT) systems. SH coefficients are precomputed and stored at object vertices, and combined interactively with SH lighting coefficients to enable effects like soft shadows, interreflections, and glossy reflection. However, the most common PRT techniques assume distant, low-frequency environment lighting, for which SH lighting coefficients can easily be computed once per frame. There is currently limited support for near-field illumination and area lights, since it is non-trivial to compute the SH coefficients for an area light, and the incident lighting (SH coefficients) varies over the object geometry. We present an efficient closed-form solution for projection of uniform polygonal area lights to spherical harmonic coefficients of arbitrary order, enabling easy adoption of accurate area lighting in PRT systems, with no modifications required to the core PRT framework. Our method only requires computing zonal harmonic (ZH) coefficients, for which we introduce a novel recurrence relation. In practice, ZH coefficients are built up iteratively, with computation linear in the desired SH order. General SH coefficients can then be obtained by the recently developed sparse zonal harmonic rotation method.

In this article, we describe a complete pipeline for the capture and display of real-world Virtual Reality video content, based on the concept of omnistereoscopic panoramas. We address important practical and theoretical issues that have remained undiscussed in previous works. On the capture side, we show how high-quality omnistereo video can be generated from a sparse set of cameras (16 in our prototype array) instead of the hundreds of input views previously required. Despite the sparse number of input views, our approach allows for high quality, real-time virtual head motion, thereby providing an important additional cue for immersive depth perception compared to static stereoscopic video. We also provide an in-depth analysis of the required camera array geometry in order to meet specific stereoscopic output constraints, which is fundamental for achieving a plausible and fully controlled VR viewing experience. Finally, we describe additional insights on how to integrate omnistereo video panoramas with rendered CG content. We provide qualitative comparisons to alternative solutions, including depth-based view synthesis and the Facebook Surround 360 system. In summary, this article provides a first complete guide and analysis for reimplementing a system for capturing and displaying real-world VR, which we demonstrate on several real-world examples captured with our prototype.

Cloth dynamics plays an important role in the visual appearance of moving characters. Properly accounting for contact and friction is of utmost importance to avoid cloth-body and cloth-cloth penetration and to capture typical folding and stick-slip behavior due to dry friction. We present here the first method able to account for cloth contact with exact Coulomb friction, treating both cloth self-contacts and contacts occurring between the cloth and an underlying character. Our key contribution is to observe that for a nodal system like cloth, the frictional contact problem may be formulated based on velocities as primary variables, without having to compute the costly Delassus operator. Then, by reversing the roles classically played by the velocities and the contact impulses, conical complementarity solvers of the literature can be adapted to solve for compatible velocities at nodes. To handle the full complexity of cloth dynamics scenarios, we have extended this base algorithm in two ways: first, towards the accurate treatment of frictional contact at any location of the cloth, through an adaptive node refinement strategy; second, towards the handling of multiple constraints at each node, through the duplication of constrained nodes and the adding of pin constraints between duplicata. Our method allows us to handle the complex cloth-cloth and cloth-body interactions in full-size garments with an unprecedented level of realism compared to former methods, while maintaining reasonable computational timings.

We present a novel extended partitioned method for two-way solid-fluid coupling, where the fluid and solid solvers are treated as black boxes with limited exposed interfaces, facilitating modularity and code reusability. Our method achieves improved stability and extended range of applicability over standard partitioned approaches through three techniques. First, we couple the black-box solvers through a small, reduced-order monolithic system, which is constructed on the fly from input/output pairs generated by the solid and fluid solvers. Second, we use a conservative, impulse-based interaction term to couple the solid and fluid rather than typical pressure-based forces. We show that both of these techniques significantly improve stability and reduce the number of iterations needed for convergence. Finally, we propose a novel boundary pressure projection method that allows for the partitioned simulation of a fully enclosed fluid coupled to a dynamic solid, a scenario that has been problematic for partitioned methods. We demonstrate the benefits of our extended partitioned method by coupling Eulerian fluid solvers for smoke and water to Lagrangian solid solvers for volumetric and thin deformable and rigid objects in a variety of challenging scenarios. We further demonstrate our method by coupling a Lagrangian SPH fluid solver to a rigid body solver.

In computer graphics the motion of the jaw is commonly modelled by up-down and left-right rotation around a fixed pivot plus a forward-backward translation, yielding a three dimensional rig that is highly suited for intuitive artistic control. The anatomical motion of the jaw is, however, much more complex since the joints that connect the jaw to the skull exhibit both rotational and translational components. In reality the jaw does not move in a three dimensional subspace but on a constrained manifold in six dimensions. We analyze this manifold in the context of computer animation and show how the manifold can be parameterized with three degrees of freedom, providing a novel jaw rig that preserves the intuitive control while providing more accurate jaw positioning. The chosen parameterization furthermore places anatomically correct limits on the motion, preventing the rig from entering physiologically infeasible poses. Our new jaw rig is empirically designed from accurate capture data, and we provide a simple method to retarget the rig to new characters, both human and fantasy.

Advection-projection methods for fluid animation are widely appreciated for their stability and efficiency. However, the projection step dissipates energy from the system, leading to artificial viscosity and suppression of small-scale details. We propose an alternative approach for detail-preserving fluid animation that is surprisingly simple and effective. We replace the energy-dissipating projection operator applied at the end of a simulation step by an energy-preserving reflection operator applied at mid-step. We show that doing so leads to two orders of magnitude reduction in energy loss, which in turn yields vastly improved detail-preservation. We evaluate our reflection solver on a set of 2D and 3D numerical experiments and show that it compares favorably to state-of-the-art methods. Finally, our method integrates seamlessly with existing projection-advection solvers and requires very little additional implementation.

We present an efficient spacetime optimization method to automatically generate animations for a general volumetric, elastically deformable body. Our approach can model the interactions between the body and the environment and automatically generate active animations. We model the frictional contact forces using contact invariant optimization and the fluid drag forces using a simplified model. To handle complex objects, we use a reduced deformable model and present a novel hybrid optimizer to search for the local minima efficiently. This allows us to use long-horizon motion planning to automatically generate animations such as walking, jumping, swimming, and rolling. We evaluate the approach on different shapes and animations, including deformable body navigation and combining with an open-loop controller for realtime forward simulation.